{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-18T11:16:01.246883Z",
     "start_time": "2024-09-18T11:16:01.239919Z"
    }
   },
   "source": [
    "import os \n",
    "os.getcwd()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\daima\\\\tianmao\\\\tianmaofugou'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:16:53.284320Z",
     "start_time": "2024-09-18T11:16:52.104269Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import gc\n",
    "import warnings\n",
    "\n",
    "\n",
    "from pylab import mpl\n",
    "# 设置显示中文字体\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "\n",
    "# 设置正常显示符号\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "warnings.filterwarnings('ignore')"
   ],
   "id": "86473f971e76c1d5",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:17:50.652562Z",
     "start_time": "2024-09-18T11:17:36.214389Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 读取数据\n",
    "test_data = pd.read_csv('test_format1.csv')\n",
    "train_data = pd.read_csv('train_format1.csv')\n",
    "\n",
    "user_info = pd.read_csv('user_info_format1.csv')\n",
    "user_log = pd.read_csv('user_log_format1.csv')"
   ],
   "id": "15ec530b469e2c91",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:26:52.675534Z",
     "start_time": "2024-09-18T11:26:52.664981Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# reduce memory\n",
    "# 这个函数通过将 DataFrame 中的数值列转换为更小的数据类型来减少内存使用，这对于处理大型数据集特别有用。在处理完这个函数后，DataFrame 会使用更少的内存，这可以加快数据处理速度并减少资源消耗\n",
    "\n",
    "# 自定义reduce_mem_usage函数优化pandas DataFrame中的数据类型减少内存使用\n",
    "# 接受两个参数：df（一个 pandas DataFrame）和 verbose（一个布尔值，表示是否打印内存使用情况）\n",
    "def reduce_mem_usage(df, verbose=True):\n",
    "    # 计算并存储 DataFrame 在优化之前的总内存使用量（以兆字节为单位）。\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "\n",
    "    # 遍历 DataFrame 的每一列。\n",
    "    for col in df.columns:\n",
    "        # 获取当前列的数据类型\n",
    "        col_type = df[col].dtypes\n",
    "        # 如果当前列的数据类型在 numerics 列表中，则继续执行优化。\n",
    "        if col_type in numerics:\n",
    "            # 获取当前列的最小值和最大值。\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            # 如果列的数据类型是整数类型，则检查其值是否适合更小的整数类型。\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                # 如果列的最小值大于 int8 的最小值且最大值小于 int8 的最大值，则将列的数据类型转换为 int8。\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                # 检查是否可以转换为 int16。\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                # 检查是否可以转换为 int32。\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                # 检查是否可以转换为 int64。\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)\n",
    "            else:\n",
    "                # 如果列的数据类型不是整数，那么它可能是浮点数类型。\n",
    "                # 如果列的最小值大于 float16 的最小值且最大值小于 float16 的最大值，则将列的数据类型转换为 float16。\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                # 检查是否可以转换为 float32。\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                # 如果都不行，则将列的数据类型转换为 float64。\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "    # 计算优化后的 DataFrame 的总内存使用量。           \n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    # 如果 verbose 为 True，则打印优化前后的内存使用情况以及减少的百分比\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    # 返回优化后的 DataFrame。\n",
    "    return df"
   ],
   "id": "72266bea7b007e60",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:27:10.365975Z",
     "start_time": "2024-09-18T11:27:07.746706Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 优化\n",
    "train_data = reduce_mem_usage(train_data)\n",
    "test_data = reduce_mem_usage(test_data)\n",
    "\n",
    "user_info = reduce_mem_usage(user_info)\n",
    "user_log = reduce_mem_usage(user_log)"
   ],
   "id": "29c57a435e802580",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage after optimization is: 1.74 MB\n",
      "Decreased by 70.8%\n",
      "Memory usage after optimization is: 3.49 MB\n",
      "Decreased by 41.7%\n",
      "Memory usage after optimization is: 3.24 MB\n",
      "Decreased by 66.7%\n",
      "Memory usage after optimization is: 890.48 MB\n",
      "Decreased by 69.6%\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:28:26.548859Z",
     "start_time": "2024-09-18T11:28:26.540769Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据探索\n",
    "train_data.head()"
   ],
   "id": "ac8edff40b3b6992",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label\n",
       "0    34176         3906      0\n",
       "1    34176          121      0\n",
       "2    34176         4356      1\n",
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   "execution_count": 8
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   "cell_type": "code",
   "source": "user_info.head()",
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       "   user_id  age_range  gender\n",
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   "cell_type": "code",
   "source": "user_log.head()",
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     "data": {
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       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2660.0         829            0\n",
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:29:02.586066Z",
     "start_time": "2024-09-18T11:29:02.566462Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 缺失值查看\n",
    "train_data.isna().sum()"
   ],
   "id": "cc4667f2b8171218",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0\n",
       "merchant_id    0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:32:59.105829Z",
     "start_time": "2024-09-18T11:32:59.089920Z"
    }
   },
   "cell_type": "code",
   "source": "test_data.isna().sum()",
   "id": "c158e791967ff857",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             0\n",
       "merchant_id         0\n",
       "prob           261477\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:34:08.152682Z",
     "start_time": "2024-09-18T11:34:08.137419Z"
    }
   },
   "cell_type": "code",
   "source": "user_info.isna().sum()/user_info.shape[0]",
   "id": "83523cd4cc38f097",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      0.000000\n",
       "age_range    0.005227\n",
       "gender       0.015173\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:34:15.820695Z",
     "start_time": "2024-09-18T11:34:15.493475Z"
    }
   },
   "cell_type": "code",
   "source": "user_log.isna().sum()/user_log.shape[0]",
   "id": "d333ddc8a49141af",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0.000000\n",
       "item_id        0.000000\n",
       "cat_id         0.000000\n",
       "seller_id      0.000000\n",
       "brand_id       0.001657\n",
       "time_stamp     0.000000\n",
       "action_type    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:35:05.388821Z",
     "start_time": "2024-09-18T11:35:05.357812Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 重复值查看\n",
    "train_data.duplicated().sum()"
   ],
   "id": "89450d1b74a53274",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:35:12.485392Z",
     "start_time": "2024-09-18T11:35:12.454102Z"
    }
   },
   "cell_type": "code",
   "source": "test_data.duplicated().sum()",
   "id": "331406fe5badc3e9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:35:18.567875Z",
     "start_time": "2024-09-18T11:35:18.519028Z"
    }
   },
   "cell_type": "code",
   "source": "user_info.duplicated().sum()",
   "id": "10d52dc9f750cb0f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:35:37.907525Z",
     "start_time": "2024-09-18T11:35:24.768570Z"
    }
   },
   "cell_type": "code",
   "source": "user_log.duplicated().sum()",
   "id": "53961e67e3be4fca",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13750198"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T11:45:29.655333Z",
     "start_time": "2024-09-18T11:45:29.218959Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可视化数据\n",
    "# 正负样本比例分布\n",
    "label_gp = train_data.groupby('label')['user_id'].count()\n",
    "print('正负样本的数量：\\n',label_gp)\n",
    "_,axe = plt.subplots(1,2,figsize=(12,6))\n",
    "train_data.label.value_counts().plot(kind='pie',autopct='%1.1f%%',shadow=True,explode=[0,0.1],ax=axe[0])\n",
    "sns.countplot(x='label',data=train_data,ax=axe[1],)"
   ],
   "id": "6a6649fa0c7cadba",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正负样本的数量：\n",
      " label\n",
      "0    244912\n",
      "1     15952\n",
      "Name: user_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "a3e7b3c058d24f14"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:05:41.511522Z",
     "start_time": "2024-09-18T12:05:41.294468Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 购买次数前5的店铺\n",
    "print('选取top5店铺\\n店铺\\t购买次数')\n",
    "print(train_data.merchant_id.value_counts().head(5))\n",
    "train_data_merchant = train_data.copy()\n",
    "train_data_merchant['TOP5'] = train_data_merchant['merchant_id'].map(lambda x: 1 if x in [4044,3828,4173,1102,4976] else 0)\n",
    "train_data_merchant = train_data_merchant[train_data_merchant['TOP5']==1]\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Merchant VS Label')\n",
    "ax = sns.countplot(x='merchant_id',hue='label',data=train_data_merchant)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()"
   ],
   "id": "2eb3d6433e718dd2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选取top5店铺\n",
      "店铺\t购买次数\n",
      "4044    3379\n",
      "3828    3254\n",
      "4173    2542\n",
      "1102    2483\n",
      "4976    1925\n",
      "Name: merchant_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:06:33.100508Z",
     "start_time": "2024-09-18T12:06:32.678295Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 复购概率分布\n",
    "user_repeat_buy = [rate for rate in train_data.groupby(['user_id'])['label'].mean() if rate <= 1 and rate > 0] \n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "#直方图\n",
    "sns.distplot(user_repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "#qq图\n",
    "res = stats.probplot(user_repeat_buy, plot=plt)"
   ],
   "id": "462ae92c46e43b73",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:11:38.637838Z",
     "start_time": "2024-09-18T12:11:38.563489Z"
    }
   },
   "cell_type": "code",
   "source": "train_data_user_info = train_data.merge(user_info,on='user_id',how='left')",
   "id": "269aca0aded277a2",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:13:12.766381Z",
     "start_time": "2024-09-18T12:13:12.329963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Gender VS Label')\n",
    "ax = sns.countplot(x='gender',hue='label',data=train_data_user_info)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height() "
   ],
   "id": "cd0af3bed6fff844",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:14:34.721537Z",
     "start_time": "2024-09-18T12:14:34.527313Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 性别与复购分布\n",
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['gender'])['label'].mean()] \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy, plot=plt)    "
   ],
   "id": "59980bdb63b7ffc8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:14:49.040074Z",
     "start_time": "2024-09-18T12:14:48.521821Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Age VS Label')\n",
    "ax = sns.countplot(x='age_range',hue='label',data=train_data_user_info)"
   ],
   "id": "af48ea06b8f9967d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:15:12.368216Z",
     "start_time": "2024-09-18T12:15:12.194647Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 年龄与复购分布\n",
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['age_range'])['label'].mean()] \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy,plot=plt)"
   ],
   "id": "223bcf6fb80a6e43",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:19:05.904027Z",
     "start_time": "2024-09-18T12:19:01.718998Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练集不同时间段的复购用户\n",
    "all_data_1 = user_log.merge(train_data,on=['user_id'],how='left')"
   ],
   "id": "46bddc13391bda56",
   "outputs": [],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:19:11.575881Z",
     "start_time": "2024-09-18T12:19:09.905768Z"
    }
   },
   "cell_type": "code",
   "source": "all_data_1[all_data_1['label'].notnull()].head()",
   "id": "18e3756376a7ac63",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type  \\\n",
       "419   234512   146770    1173        693    3186.0         625            0   \n",
       "420   234512   146770    1173        693    3186.0         625            0   \n",
       "421   234512  1106076     992       3783    8164.0        1016            0   \n",
       "422   234512  1106076     992       3783    8164.0        1016            0   \n",
       "423   234512   866567    1198        693    3186.0         625            0   \n",
       "\n",
       "     merchant_id  label  \n",
       "419       3018.0    0.0  \n",
       "420       3271.0    0.0  \n",
       "421       3018.0    0.0  \n",
       "422       3271.0    0.0  \n",
       "423       3018.0    0.0  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>234512</td>\n",
       "      <td>866567</td>\n",
       "      <td>1198</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:19:21.248027Z",
     "start_time": "2024-09-18T12:19:20.279511Z"
    }
   },
   "cell_type": "code",
   "source": "all_data_2=all_data_1[all_data_1['label'].notnull()]",
   "id": "da5bb6a460f34997",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:20:39.335911Z",
     "start_time": "2024-09-18T12:20:38.923332Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum=all_data_2.groupby(['time_stamp'])['label'].sum().reset_index()\n",
    "all_data_2_sum.head()"
   ],
   "id": "c428af9cf5d5537e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   time_stamp   label\n",
       "0         511   943.0\n",
       "1         512   975.0\n",
       "2         513  1221.0\n",
       "3         514  1170.0\n",
       "4         515  1260.0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
       "      <td>943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>512</td>\n",
       "      <td>975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513</td>\n",
       "      <td>1221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>514</td>\n",
       "      <td>1170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>515</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:20:41.923533Z",
     "start_time": "2024-09-18T12:20:41.913255Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum['time_stamp'] = all_data_2_sum['time_stamp'].astype(str)\n",
    "all_data_2_sum['label'] = all_data_2_sum['label'].astype(int)"
   ],
   "id": "f9a90ebd40640fd6",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:21:01.889118Z",
     "start_time": "2024-09-18T12:21:01.870812Z"
    }
   },
   "cell_type": "code",
   "source": [
    "a=[]\n",
    "for i in range(len(all_data_2_sum)):\n",
    "    if len(all_data_2_sum['time_stamp'][i])==3:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0])\n",
    "    else:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0:2])"
   ],
   "id": "b507a422222eadbb",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:21:10.215561Z",
     "start_time": "2024-09-18T12:21:10.203665Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum['month']=a\n",
    "all_data_2_sum=all_data_2_sum.astype(int)"
   ],
   "id": "27fd5f3bbdb047e",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:21:19.779621Z",
     "start_time": "2024-09-18T12:21:19.240164Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "c=5\n",
    "for i in range(1,8):\n",
    "    \n",
    "    plt.subplot(3, 3, i)\n",
    "    b=all_data_2_sum[all_data_2_sum[\"month\"]==c]\n",
    "    plt.plot(b['time_stamp'], b['label'], linewidth=1, color=\"orange\", marker=\"o\",label=\"Mean value\")\n",
    "  \n",
    "    c+=1"
   ],
   "id": "5e7905d0a87fd985",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 7 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:22:27.657636Z",
     "start_time": "2024-09-18T12:22:27.559420Z"
    }
   },
   "cell_type": "code",
   "source": [
    "c=all_data_2_sum.groupby(['month'])['label'].sum().reset_index()\n",
    "plt.plot(c['month'], c['label'], linewidth=1, color=\"orange\", marker=\"o\",label=\"Mean value\")"
   ],
   "id": "9c98ddcb960877f3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f68bda22b0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "2194689d25c1118e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:22:53.263192Z",
     "start_time": "2024-09-18T12:22:53.076673Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 特征工程\n",
    "\n",
    "# 这行代码从 test_data DataFrame 中删除名为 ‘prob’ 的列\n",
    "del test_data['prob']\n",
    "# 这两行代码分别将 train_data 和 test_data 中的 ‘target’ 列设置为 1 和 -1。这通常是为了标记训练集和测试集。\n",
    "train_data['target'] = 1\n",
    "test_data['target']=-1\n",
    "# 这行代码将 train_data 和 test_data 合并成一个新 DataFrame，命名为 all_data。这通常是为了将训练集和测试集放在一起进行后续的分析或处理。\n",
    "all_data = train_data.append(test_data)\n",
    "# 这行代码使用 merge 函数将 all_data 与 user_info DataFrame 合并，基于 ‘user_id’ 列进行匹配。‘how’ 参数设置为 ‘left’ 表示左连接，这意味着 all_data 中的 ‘user_id’ 列将保持不变，而 user_info 中的 ‘user_id’ 列将与 all_data 中的 ‘user_id’ 列进行匹配。\n",
    "all_data = all_data.merge(user_info,on=['user_id'],how='left')\n",
    "# 这行代码从内存中删除 train_data、test_data 和 user_info 这三个 DataFrame。这是为了释放内存，特别是在处理大型数据集时。\n",
    "del train_data, test_data, user_info\n",
    "# 这行代码调用 Python 的垃圾收集器（garbage collector）来回收不再使用的内存。这有助于进一步释放内存，尤其是在删除大型对象后。\n",
    "gc.collect()"
   ],
   "id": "42a1d72d348f643a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28657"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:26:04.366015Z",
     "start_time": "2024-09-18T12:26:04.353998Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "e36cb71f894f7d4e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender\n",
       "0    34176         3906    0.0       1        6.0     0.0\n",
       "1    34176          121    0.0       1        6.0     0.0\n",
       "2    34176         4356    1.0       1        6.0     0.0\n",
       "3    34176         2217    0.0       1        6.0     0.0\n",
       "4   230784         4818    0.0       1        0.0     0.0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:28:21.098938Z",
     "start_time": "2024-09-18T12:28:21.079344Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.dropna(subset=['age_range','gender'],inplace=True)",
   "id": "91733411aa22a0fe",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:28:29.242545Z",
     "start_time": "2024-09-18T12:28:29.219840Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.isnull().sum()",
   "id": "d4b24a64379a3f6a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             0\n",
       "merchant_id         0\n",
       "label          257626\n",
       "target              0\n",
       "age_range           0\n",
       "gender              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:28:41.234465Z",
     "start_time": "2024-09-18T12:28:40.489859Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 用户店铺数\n",
    "sell= user_log.groupby(['user_id'])['seller_id'].count().reset_index()\n",
    "\n",
    "all_data = all_data.merge(sell,on=['user_id'],how='inner')\n",
    "all_data.rename(columns={'seller_id':'sell_sum'},inplace=True)\n",
    "all_data.head()"
   ],
   "id": "a9e23425391f9d7d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum\n",
       "0    34176         3906    0.0       1        6.0     0.0       451\n",
       "1    34176          121    0.0       1        6.0     0.0       451\n",
       "2    34176         4356    1.0       1        6.0     0.0       451\n",
       "3    34176         2217    0.0       1        6.0     0.0       451\n",
       "4   230784         4818    0.0       1        0.0     0.0        54"
      ],
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       "      <th>user_id</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
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      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 43
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  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:31:43.012492Z",
     "start_time": "2024-09-18T12:31:42.997978Z"
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   "source": [
    "# 定义一个函数 nunique_k，它接受三个参数：data（一个 pandas DataFrame），sigle_name（要计算唯一值的列名）和 new_name_1（新列的名称）\n",
    "def nunique_k(data,sigle_name,new_name_1):\n",
    "    # 这行代码使用 groupby 方法对 user_log DataFrame 按 ‘user_id’ 列进行分组，然后对每个组计算 sigle_name 列的唯一值数量。nunique() 函数用于计算唯一值的数量，reset_index() 方法用于将结果重置为 DataFrame 格式。\n",
    "    data1=user_log.groupby(['user_id'])[sigle_name].nunique().reset_index()\n",
    "    \n",
    "    # 这行代码使用 merge 函数将原始 DataFrame data 与 data1 合并。合并基于 ‘user_id’ 列，并使用内连接（inner join）方式。\n",
    "    data_union=data.merge(data1,on=['user_id'],how='inner')\n",
    "    \n",
    "    # 这行代码将 data_union DataFrame 中的 sigle_name 列重命名为 new_name_1。rename() 函数用于重命名列，inplace=True 参数表示更改将在原 DataFrame 上进行，而不是创建一个新的 DataFrame。\n",
    "    data_union.rename(columns={sigle_name:new_name_1},inplace=True)\n",
    "    \n",
    "    # 函数返回经过操作后的 data_union DataFrame。\n",
    "    return data_union"
   ],
   "id": "3f872b86414e0600",
   "outputs": [],
   "execution_count": 45
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:04.614715Z",
     "start_time": "2024-09-18T12:31:53.194226Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 不同店铺个数\n",
    "all_data=nunique_k(all_data,'seller_id','seller_id_unique')"
   ],
   "id": "8618bf166ca8ddca",
   "outputs": [],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:14.567220Z",
     "start_time": "2024-09-18T12:32:04.615649Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 不同品类个数\n",
    "all_data=nunique_k(all_data,'cat_id','cat_id_unique')"
   ],
   "id": "a957b458de8953af",
   "outputs": [],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:26.315766Z",
     "start_time": "2024-09-18T12:32:14.840816Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 不同品牌个数\n",
    "all_data=nunique_k(all_data,'brand_id','brand_id_unique')"
   ],
   "id": "19c27a80820777d6",
   "outputs": [],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:43.847963Z",
     "start_time": "2024-09-18T12:32:26.316767Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 不同商品个数\n",
    "all_data=nunique_k(all_data,'item_id','item_id_unique')"
   ],
   "id": "e14611048127baf6",
   "outputs": [],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:51.983849Z",
     "start_time": "2024-09-18T12:32:43.848958Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 活跃天数\n",
    "all_data=nunique_k(all_data,'time_stamp','time_stamp_unique')"
   ],
   "id": "3c0ebd43f6fe5f0d",
   "outputs": [],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:55.959867Z",
     "start_time": "2024-09-18T12:32:51.984593Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 不用行为种数\n",
    "all_data=nunique_k(all_data,'action_type','action_type_unique')"
   ],
   "id": "63e3b23df1befeb7",
   "outputs": [],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:55.975442Z",
     "start_time": "2024-09-18T12:32:55.962878Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "e66b735511b9b948",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  item_id_unique  \\\n",
       "0               109             45              106             256   \n",
       "1               109             45              106             256   \n",
       "2               109             45              106             256   \n",
       "3               109             45              106             256   \n",
       "4                20             17               19              31   \n",
       "\n",
       "   time_stamp_unique  action_type_unique  \n",
       "0                 47                   3  \n",
       "1                 47                   3  \n",
       "2                 47                   3  \n",
       "3                 47                   3  \n",
       "4                 16                   2  "
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       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:32:59.176194Z",
     "start_time": "2024-09-18T12:32:59.168261Z"
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   },
   "cell_type": "code",
   "source": "user_log.head()",
   "id": "bed2cfa9a4e1e646",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2660.0         829            0\n",
       "1   328862   844400    1271       2882    2660.0         829            0\n",
       "2   328862   575153    1271       2882    2660.0         829            0\n",
       "3   328862   996875    1271       2882    2660.0         829            0\n",
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       "      <td>0</td>\n",
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       "</table>\n",
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:33:11.907255Z",
     "start_time": "2024-09-18T12:33:10.901663Z"
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   },
   "cell_type": "code",
   "source": [
    "# 活跃天数方差\n",
    "std=user_log.groupby(['user_id'])['time_stamp'].std().reset_index()\n",
    "all_data = all_data.merge(std,on=['user_id'],how='inner')\n",
    "all_data.rename(columns={'time_stamp':'time_stamp_std'},inplace=True)\n",
    "all_data.head()"
   ],
   "id": "61d5d9b0dbee1d3e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  item_id_unique  \\\n",
       "0               109             45              106             256   \n",
       "1               109             45              106             256   \n",
       "2               109             45              106             256   \n",
       "3               109             45              106             256   \n",
       "4                20             17               19              31   \n",
       "\n",
       "   time_stamp_unique  action_type_unique  time_stamp_std  \n",
       "0                 47                   3      206.449449  \n",
       "1                 47                   3      206.449449  \n",
       "2                 47                   3      206.449449  \n",
       "3                 47                   3      206.449449  \n",
       "4                 16                   2      218.341897  "
      ],
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       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
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       "      <th>age_range</th>\n",
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       "      <th>brand_id_unique</th>\n",
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 54
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "''' a=[1,2,4,5,6,4,6,8,9,1,1,2,5,9]\n",
    " b=Counter(a)\n",
    " print(b)\n",
    " print(b.most_common(1)[0][1])'''"
   ],
   "id": "987a51c4b6e2bd7e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:33:36.002579Z",
     "start_time": "2024-09-18T12:33:35.993571Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def most_love(data_1,most_name,new_name_2):\n",
    "    data2=user_log.groupby(['user_id'])[most_name].apply(lambda x: Counter(x).most_common(1)[0][0]).reset_index()\n",
    "    data_union_1=data_1.merge(data2,on=['user_id'],how='inner')\n",
    "    data_union_1.rename(columns={most_name:new_name_2},inplace=True)\n",
    "    return data_union_1"
   ],
   "id": "1baed0b34740c955",
   "outputs": [],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:33:54.417759Z",
     "start_time": "2024-09-18T12:33:42.674484Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 用户最喜欢的店铺\n",
    "all_data=most_love(all_data,'seller_id','sell_id_most')"
   ],
   "id": "7bd0aa9d41e20a9",
   "outputs": [],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:34:05.837747Z",
     "start_time": "2024-09-18T12:33:54.418721Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的类目\n",
    "all_data=most_love(all_data,'cat_id','cat_id_most')"
   ],
   "id": "f85aa418a59fff46",
   "outputs": [],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:34:18.328197Z",
     "start_time": "2024-09-18T12:34:05.838809Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的品牌\n",
    "all_data=most_love(all_data,'brand_id','brand_id_most')"
   ],
   "id": "2b9f0962d0abc127",
   "outputs": [],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:34:27.228040Z",
     "start_time": "2024-09-18T12:34:18.330123Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最常见的行为动作\n",
    "all_data=most_love(all_data,'action_type','action_type_most')"
   ],
   "id": "cc342f7e90bc56fd",
   "outputs": [],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:34:27.243234Z",
     "start_time": "2024-09-18T12:34:27.230032Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "a9b03c1932a19801",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  item_id_unique  \\\n",
       "0               109             45              106             256   \n",
       "1               109             45              106             256   \n",
       "2               109             45              106             256   \n",
       "3               109             45              106             256   \n",
       "4                20             17               19              31   \n",
       "\n",
       "   time_stamp_unique  action_type_unique  time_stamp_std  sell_id_most  \\\n",
       "0                 47                   3      206.449449           331   \n",
       "1                 47                   3      206.449449           331   \n",
       "2                 47                   3      206.449449           331   \n",
       "3                 47                   3      206.449449           331   \n",
       "4                 16                   2      218.341897          3556   \n",
       "\n",
       "   cat_id_most  brand_id_most  action_type_most  \n",
       "0          662         4094.0                 0  \n",
       "1          662         4094.0                 0  \n",
       "2          662         4094.0                 0  \n",
       "3          662         4094.0                 0  \n",
       "4          407         1236.0                 0  "
      ],
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       "<div>\n",
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     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 60
  },
  {
   "metadata": {
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     "end_time": "2024-09-18T12:34:27.259259Z",
     "start_time": "2024-09-18T12:34:27.244237Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def most_love_cnt(data_1,most_name,new_name_2):\n",
    "    data2=user_log.groupby(['user_id'])[most_name].apply(lambda x: Counter(x).most_common(1)[0][1]).reset_index()\n",
    "    data_union_1=data_1.merge(data2,on=['user_id'],how='inner')\n",
    "    data_union_1.rename(columns={most_name:new_name_2},inplace=True)\n",
    "    return data_union_1"
   ],
   "id": "239ea5992454ba87",
   "outputs": [],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:39:52.662377Z",
     "start_time": "2024-09-18T12:39:42.007265Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 用户最喜欢的店铺 行为次数\n",
    "all_data=most_love_cnt(all_data,'seller_id','seller_id_most_cnt')"
   ],
   "id": "814092964a7cd087",
   "outputs": [],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:52:31.075237Z",
     "start_time": "2024-09-18T12:52:19.652305Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的类目 行为次数\n",
    "all_data=most_love_cnt(all_data,'cat_id','cat_id_most_cnt')"
   ],
   "id": "5656a1b5475f3b63",
   "outputs": [],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:52:43.414252Z",
     "start_time": "2024-09-18T12:52:31.076262Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的品牌 行为次数\n",
    "all_data=most_love_cnt(all_data,'brand_id','brand_id_most_cnt')"
   ],
   "id": "49ff9bedeef5a309",
   "outputs": [],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:52:52.450609Z",
     "start_time": "2024-09-18T12:52:43.416242Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最常见的行为动作 行为次数\n",
    "all_data=most_love_cnt(all_data,'action_type','action_type_most_cnt')"
   ],
   "id": "44abf58f1203239d",
   "outputs": [],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:52:52.466312Z",
     "start_time": "2024-09-18T12:52:52.452112Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "96bc2868a5421b14",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  ...  time_stamp_std  \\\n",
       "0               109             45              106  ...      206.449449   \n",
       "1               109             45              106  ...      206.449449   \n",
       "2               109             45              106  ...      206.449449   \n",
       "3               109             45              106  ...      206.449449   \n",
       "4                20             17               19  ...      218.341897   \n",
       "\n",
       "   sell_id_most  cat_id_most  brand_id_most  action_type_most  \\\n",
       "0           331          662         4094.0                 0   \n",
       "1           331          662         4094.0                 0   \n",
       "2           331          662         4094.0                 0   \n",
       "3           331          662         4094.0                 0   \n",
       "4          3556          407         1236.0                 0   \n",
       "\n",
       "   seller_id_most_cnt  seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                  70                  70               98                 70   \n",
       "1                  70                  70               98                 70   \n",
       "2                  70                  70               98                 70   \n",
       "3                  70                  70               98                 70   \n",
       "4                  10                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  \n",
       "0                   410  \n",
       "1                   410  \n",
       "2                   410  \n",
       "3                   410  \n",
       "4                    47  \n",
       "\n",
       "[5 rows x 23 columns]"
      ],
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       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
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       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
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       "      <td>106</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>109</td>\n",
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       "      <td>...</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
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       "      <td>17</td>\n",
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       "      <td>3556</td>\n",
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       "      <td>47</td>\n",
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       "</div>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:53:19.258531Z",
     "start_time": "2024-09-18T12:53:19.176691Z"
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   },
   "cell_type": "code",
   "source": "user_id_union=list(set(all_data['user_id']))",
   "id": "a02360b3c90428de",
   "outputs": [],
   "execution_count": 68
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:53:24.801512Z",
     "start_time": "2024-09-18T12:53:24.793502Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def action_type_select(data,num,new_name):\n",
    "    d=user_log.groupby(['user_id'])['action_type'].apply(lambda x: Counter(x))\n",
    "    e=dict(d)\n",
    "    k=[]\n",
    "    for i in user_id_union:\n",
    "        try: \n",
    "            k.append(e[(i,num)])\n",
    "        except KeyError:\n",
    "            k.append(0) \n",
    "    data3=pd.DataFrame({'user_id':user_id_union,new_name:k})\n",
    "    data_union_2=data.merge(data3,on=['user_id'],how='inner')\n",
    "    return data_union_2"
   ],
   "id": "8703a4908e8878b",
   "outputs": [],
   "execution_count": 69
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:53:58.365452Z",
     "start_time": "2024-09-18T12:53:31.600484Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 点击次数\n",
    "all_data=action_type_select(all_data,0,'action_type_sum_0')"
   ],
   "id": "cc2d5ed2acc1897b",
   "outputs": [],
   "execution_count": 70
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:54:25.413841Z",
     "start_time": "2024-09-18T12:53:58.368452Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 加购次数\n",
    "all_data=action_type_select(all_data,1,'action_type_sum_1')"
   ],
   "id": "68689de3267780e0",
   "outputs": [],
   "execution_count": 71
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:54:50.719304Z",
     "start_time": "2024-09-18T12:54:25.414924Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 购买次数\n",
    "all_data=action_type_select(all_data,2,'action_type_sum_2')"
   ],
   "id": "f674b9fe4735a30d",
   "outputs": [],
   "execution_count": 72
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:55:13.849436Z",
     "start_time": "2024-09-18T12:54:50.720304Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 收藏次数\n",
    "all_data=action_type_select(all_data,3,'action_type_sum_2')"
   ],
   "id": "4c7e3c54d726de85",
   "outputs": [],
   "execution_count": 73
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 去重重复列\n",
    "'''all_data=all_data.T.drop_duplicates(keep='first').T'''"
   ],
   "id": "c53cbecdb6aa4456"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:55:13.865087Z",
     "start_time": "2024-09-18T12:55:13.850342Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "3e498804dea14c45",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  ...  action_type_most  \\\n",
       "0               109             45              106  ...                 0   \n",
       "1               109             45              106  ...                 0   \n",
       "2               109             45              106  ...                 0   \n",
       "3               109             45              106  ...                 0   \n",
       "4                20             17               19  ...                 0   \n",
       "\n",
       "   seller_id_most_cnt  seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                  70                  70               98                 70   \n",
       "1                  70                  70               98                 70   \n",
       "2                  70                  70               98                 70   \n",
       "3                  70                  70               98                 70   \n",
       "4                  10                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                   410              410.0                NaN   \n",
       "1                   410              410.0                NaN   \n",
       "2                   410              410.0                NaN   \n",
       "3                   410              410.0                NaN   \n",
       "4                    47               47.0                NaN   \n",
       "\n",
       "   action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                 34.0                  7.0  \n",
       "1                 34.0                  7.0  \n",
       "2                 34.0                  7.0  \n",
       "3                 34.0                  7.0  \n",
       "4                  7.0                  NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
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       "    <tr>\n",
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       "      <td>34176</td>\n",
       "      <td>121</td>\n",
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       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
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       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
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       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 74
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:09.217223Z",
     "start_time": "2024-09-18T12:56:09.123473Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train = all_data[all_data['target'] == 1].reset_index(drop = True)\n",
    "test = all_data[all_data['target'] == -1].reset_index(drop = True)"
   ],
   "id": "c1f2350b1ba249a2",
   "outputs": [],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:15.919203Z",
     "start_time": "2024-09-18T12:56:15.885466Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train.drop(['target'],axis=1,inplace=True)\n",
    "test.drop(['target'],axis=1,inplace=True)"
   ],
   "id": "bd058d0703e1f726",
   "outputs": [],
   "execution_count": 76
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:22.331990Z",
     "start_time": "2024-09-18T12:56:22.301467Z"
    }
   },
   "cell_type": "code",
   "source": "train.fillna(0,inplace=True)",
   "id": "7f0876817fb1f1cf",
   "outputs": [],
   "execution_count": 77
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:28.588517Z",
     "start_time": "2024-09-18T12:56:28.559021Z"
    }
   },
   "cell_type": "code",
   "source": "test.drop(['label'],axis=1,inplace=True)",
   "id": "292c6f725072b885",
   "outputs": [],
   "execution_count": 78
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:34.459109Z",
     "start_time": "2024-09-18T12:56:34.444579Z"
    }
   },
   "cell_type": "code",
   "source": "test.fillna(0,inplace=True)",
   "id": "44ef98bea4e49e22",
   "outputs": [],
   "execution_count": 79
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:41.714648Z",
     "start_time": "2024-09-18T12:56:41.699271Z"
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   },
   "cell_type": "code",
   "source": "test.head()",
   "id": "faf2b1f502b796dd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0   163968         4605        0.0     0.0        81                21   \n",
       "1   360576         1581        2.0     2.0        77                37   \n",
       "2    98688         1964        6.0     0.0        56                22   \n",
       "3    98688         3645        6.0     0.0        56                22   \n",
       "4   295296         3361        2.0     1.0       176                56   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  time_stamp_unique  ...  \\\n",
       "0             21               22              34                 26  ...   \n",
       "1             27               37              65                 22  ...   \n",
       "2             18               21              25                 10  ...   \n",
       "3             18               21              25                 10  ...   \n",
       "4             32               46              85                 33  ...   \n",
       "\n",
       "   action_type_most  seller_id_most_cnt  seller_id_most_cnt  cat_id_most_cnt  \\\n",
       "0                 0                  22                  22               17   \n",
       "1                 0                  10                  10               13   \n",
       "2                 0                  11                  11               11   \n",
       "3                 0                  11                  11               11   \n",
       "4                 0                  50                  50               59   \n",
       "\n",
       "   brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                 22                    63               63.0   \n",
       "1                 10                    71               71.0   \n",
       "2                 11                    51               51.0   \n",
       "3                 11                    51               51.0   \n",
       "4                 49                   162              162.0   \n",
       "\n",
       "   action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                0.0                 16.0                  2.0  \n",
       "1                0.0                  6.0                  0.0  \n",
       "2                0.0                  5.0                  0.0  \n",
       "3                0.0                  5.0                  0.0  \n",
       "4                0.0                  7.0                  7.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
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       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360576</td>\n",
       "      <td>1581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77</td>\n",
       "      <td>37</td>\n",
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       "      <td>37</td>\n",
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       "      <td>11</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295296</td>\n",
       "      <td>3361</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 80
  },
  {
   "metadata": {
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     "end_time": "2024-09-18T12:56:52.987504Z",
     "start_time": "2024-09-18T12:56:50.634282Z"
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   },
   "cell_type": "code",
   "source": [
    "train.to_csv('train_all_k.csv',header=True,index=False)\n",
    "test.to_csv('test_all_k.csv',header=True,index=False)"
   ],
   "id": "c342fd1e4785bc76",
   "outputs": [],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:56:59.276303Z",
     "start_time": "2024-09-18T12:56:58.970043Z"
    }
   },
   "cell_type": "code",
   "source": "pd.read_csv('train_all_k.csv').head()",
   "id": "82154d749fd0e09",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0    34176         3906    0.0        6.0     0.0       451               109   \n",
       "1    34176          121    0.0        6.0     0.0       451               109   \n",
       "2    34176         4356    1.0        6.0     0.0       451               109   \n",
       "3    34176         2217    0.0        6.0     0.0       451               109   \n",
       "4   230784         4818    0.0        0.0     0.0        54                20   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  ...  action_type_most  \\\n",
       "0             45              106             256  ...                 0   \n",
       "1             45              106             256  ...                 0   \n",
       "2             45              106             256  ...                 0   \n",
       "3             45              106             256  ...                 0   \n",
       "4             17               19              31  ...                 0   \n",
       "\n",
       "   seller_id_most_cnt  seller_id_most_cnt.1  cat_id_most_cnt  \\\n",
       "0                  70                    70               98   \n",
       "1                  70                    70               98   \n",
       "2                  70                    70               98   \n",
       "3                  70                    70               98   \n",
       "4                  10                    10                9   \n",
       "\n",
       "   brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                 70                   410              410.0   \n",
       "1                 70                   410              410.0   \n",
       "2                 70                   410              410.0   \n",
       "3                 70                   410              410.0   \n",
       "4                 10                    47               47.0   \n",
       "\n",
       "   action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                0.0                 34.0                  7.0  \n",
       "1                0.0                 34.0                  7.0  \n",
       "2                0.0                 34.0                  7.0  \n",
       "3                0.0                 34.0                  7.0  \n",
       "4                0.0                  7.0                  0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ],
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
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       "      <th>seller_id_most_cnt.1</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
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       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>256</td>\n",
       "      <td>...</td>\n",
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       "      <td>98</td>\n",
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       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 82
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:57:19.248130Z",
     "start_time": "2024-09-18T12:57:18.945411Z"
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   },
   "cell_type": "code",
   "source": [
    "# 建模\n",
    "train_data=pd.read_csv('train_all_k.csv')\n",
    "train_data.head()"
   ],
   "id": "8cd6df179ecbd04c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0    34176         3906    0.0        6.0     0.0       451               109   \n",
       "1    34176          121    0.0        6.0     0.0       451               109   \n",
       "2    34176         4356    1.0        6.0     0.0       451               109   \n",
       "3    34176         2217    0.0        6.0     0.0       451               109   \n",
       "4   230784         4818    0.0        0.0     0.0        54                20   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  ...  action_type_most  \\\n",
       "0             45              106             256  ...                 0   \n",
       "1             45              106             256  ...                 0   \n",
       "2             45              106             256  ...                 0   \n",
       "3             45              106             256  ...                 0   \n",
       "4             17               19              31  ...                 0   \n",
       "\n",
       "   seller_id_most_cnt  seller_id_most_cnt.1  cat_id_most_cnt  \\\n",
       "0                  70                    70               98   \n",
       "1                  70                    70               98   \n",
       "2                  70                    70               98   \n",
       "3                  70                    70               98   \n",
       "4                  10                    10                9   \n",
       "\n",
       "   brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                 70                   410              410.0   \n",
       "1                 70                   410              410.0   \n",
       "2                 70                   410              410.0   \n",
       "3                 70                   410              410.0   \n",
       "4                 10                    47               47.0   \n",
       "\n",
       "   action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                0.0                 34.0                  7.0  \n",
       "1                0.0                 34.0                  7.0  \n",
       "2                0.0                 34.0                  7.0  \n",
       "3                0.0                 34.0                  7.0  \n",
       "4                0.0                  7.0                  0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ],
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
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       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>seller_id_most_cnt.1</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 83
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:57:27.631656Z",
     "start_time": "2024-09-18T12:57:27.340092Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_data=pd.read_csv('test_all_k.csv')\n",
    "test_data.head()"
   ],
   "id": "e58b70813716e1a6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0   163968         4605        0.0     0.0        81                21   \n",
       "1   360576         1581        2.0     2.0        77                37   \n",
       "2    98688         1964        6.0     0.0        56                22   \n",
       "3    98688         3645        6.0     0.0        56                22   \n",
       "4   295296         3361        2.0     1.0       176                56   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  time_stamp_unique  ...  \\\n",
       "0             21               22              34                 26  ...   \n",
       "1             27               37              65                 22  ...   \n",
       "2             18               21              25                 10  ...   \n",
       "3             18               21              25                 10  ...   \n",
       "4             32               46              85                 33  ...   \n",
       "\n",
       "   action_type_most  seller_id_most_cnt  seller_id_most_cnt.1  \\\n",
       "0                 0                  22                    22   \n",
       "1                 0                  10                    10   \n",
       "2                 0                  11                    11   \n",
       "3                 0                  11                    11   \n",
       "4                 0                  50                    50   \n",
       "\n",
       "   cat_id_most_cnt  brand_id_most_cnt  action_type_most_cnt  \\\n",
       "0               17                 22                    63   \n",
       "1               13                 10                    71   \n",
       "2               11                 11                    51   \n",
       "3               11                 11                    51   \n",
       "4               59                 49                   162   \n",
       "\n",
       "   action_type_sum_0  action_type_sum_1  action_type_sum_2_x  \\\n",
       "0               63.0                0.0                 16.0   \n",
       "1               71.0                0.0                  6.0   \n",
       "2               51.0                0.0                  5.0   \n",
       "3               51.0                0.0                  5.0   \n",
       "4              162.0                0.0                  7.0   \n",
       "\n",
       "   action_type_sum_2_y  \n",
       "0                  2.0  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  7.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>seller_id_most_cnt.1</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163968</td>\n",
       "      <td>4605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360576</td>\n",
       "      <td>1581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>71</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98688</td>\n",
       "      <td>1964</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295296</td>\n",
       "      <td>3361</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>176</td>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>59</td>\n",
       "      <td>49</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 84
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T12:57:34.677864Z",
     "start_time": "2024-09-18T12:57:34.658100Z"
    }
   },
   "cell_type": "code",
   "source": "train_data['label'].value_counts()",
   "id": "55e14e9f6cb782cf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    241303\n",
       "1.0     15838\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 85
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:21:33.415682Z",
     "start_time": "2024-09-18T13:21:33.332240Z"
    }
   },
   "cell_type": "code",
   "source": [
    "target = train_data['label']\n",
    "target.head\n",
    "train.fillna(0,inplace=True)\n",
    "train_data.drop(['label'],axis=1,inplace=True)\n",
    "train_data.drop(['user_id'],axis=1,inplace=True)\n",
    "train_data.drop(['merchant_id'],axis=1,inplace=True)\n",
    "train_data.head"
   ],
   "id": "c6a24f50c07394f6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of         age_range  gender  sell_sum  seller_id_unique  cat_id_unique  \\\n",
       "0             6.0     0.0       451               109             45   \n",
       "1             6.0     0.0       451               109             45   \n",
       "2             6.0     0.0       451               109             45   \n",
       "3             6.0     0.0       451               109             45   \n",
       "4             0.0     0.0        54                20             17   \n",
       "...           ...     ...       ...               ...            ...   \n",
       "257136        4.0     1.0       117                33             25   \n",
       "257137        0.0     1.0       198                38             20   \n",
       "257138        0.0     1.0       198                38             20   \n",
       "257139        0.0     1.0       198                38             20   \n",
       "257140        4.0     2.0       194                50             29   \n",
       "\n",
       "        brand_id_unique  item_id_unique  time_stamp_unique  \\\n",
       "0                   106             256                 47   \n",
       "1                   106             256                 47   \n",
       "2                   106             256                 47   \n",
       "3                   106             256                 47   \n",
       "4                    19              31                 16   \n",
       "...                 ...             ...                ...   \n",
       "257136               32              49                 12   \n",
       "257137               36              89                  6   \n",
       "257138               36              89                  6   \n",
       "257139               36              89                  6   \n",
       "257140               49             127                 23   \n",
       "\n",
       "        action_type_unique  time_stamp_std  ...  action_type_most  \\\n",
       "0                        3      206.449449  ...                 0   \n",
       "1                        3      206.449449  ...                 0   \n",
       "2                        3      206.449449  ...                 0   \n",
       "3                        3      206.449449  ...                 0   \n",
       "4                        2      218.341897  ...                 0   \n",
       "...                    ...             ...  ...               ...   \n",
       "257136                   3       60.319805  ...                 0   \n",
       "257137                   3       55.674024  ...                 0   \n",
       "257138                   3       55.674024  ...                 0   \n",
       "257139                   3       55.674024  ...                 0   \n",
       "257140                   3      201.170042  ...                 0   \n",
       "\n",
       "        seller_id_most_cnt  seller_id_most_cnt.1  cat_id_most_cnt  \\\n",
       "0                       70                    70               98   \n",
       "1                       70                    70               98   \n",
       "2                       70                    70               98   \n",
       "3                       70                    70               98   \n",
       "4                       10                    10                9   \n",
       "...                    ...                   ...              ...   \n",
       "257136                  22                    22               15   \n",
       "257137                  28                    28               38   \n",
       "257138                  28                    28               38   \n",
       "257139                  28                    28               38   \n",
       "257140                  24                    24               33   \n",
       "\n",
       "        brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                      70                   410              410.0   \n",
       "1                      70                   410              410.0   \n",
       "2                      70                   410              410.0   \n",
       "3                      70                   410              410.0   \n",
       "4                      10                    47               47.0   \n",
       "...                   ...                   ...                ...   \n",
       "257136                 25                   107              107.0   \n",
       "257137                 28                   162              162.0   \n",
       "257138                 28                   162              162.0   \n",
       "257139                 28                   162              162.0   \n",
       "257140                 24                   181              181.0   \n",
       "\n",
       "        action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                     0.0                 34.0                  7.0  \n",
       "1                     0.0                 34.0                  7.0  \n",
       "2                     0.0                 34.0                  7.0  \n",
       "3                     0.0                 34.0                  7.0  \n",
       "4                     0.0                  7.0                  0.0  \n",
       "...                   ...                  ...                  ...  \n",
       "257136                0.0                  9.0                  1.0  \n",
       "257137                0.0                  5.0                 31.0  \n",
       "257138                0.0                  5.0                 31.0  \n",
       "257139                0.0                  5.0                 31.0  \n",
       "257140                0.0                  9.0                  4.0  \n",
       "\n",
       "[257141 rows x 23 columns]>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 88
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:22:13.503542Z",
     "start_time": "2024-09-18T13:22:12.427465Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from functools import partial\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "# from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "import lightgbm\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn import  metrics \n",
    "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score, confusion_matrix, log_loss,  auc, precision_recall_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "id": "2823213481276d83",
   "outputs": [],
   "execution_count": 89
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def model_clf(model):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_train_pred = model.predict_proba(X_train)\n",
    "    y_train_pred_pos = y_train_pred[:,1]\n",
    "\n",
    "    y_test_pred = model.predict_proba(X_test)\n",
    "    y_test_pred_pos = y_test_pred[:,1]\n",
    "\n",
    "    auc_train = roc_auc_score(y_train, y_train_pred_pos)#AUC评分\n",
    "    auc_test = roc_auc_score(y_test, y_test_pred_pos)\n",
    "\n",
    "    print(f\"Train AUC Score {auc_train}\")\n",
    "    print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_test,y_test_pred_pos)#绘制ROC曲线\n",
    "    return fpr,tpr"
   ],
   "id": "393f218d17a262a6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:22:35.958836Z",
     "start_time": "2024-09-18T13:22:34.430404Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 逻辑斯蒂回归\n",
    "stdScaler = StandardScaler()\n",
    "\n",
    "X = stdScaler.fit_transform(train_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)\n",
    "# Split the data into a training set and a test set\n",
    "\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')\n",
    "model_clf(clf)"
   ],
   "id": "79456580122e4fd1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5845234222885016\n",
      "Test AUC Score 0.5791518463942217\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.65510849e-05, 1.65510849e-05, ...,\n",
       "        9.99917245e-01, 9.99950347e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 0.00000000e+00, 2.58598397e-04, ...,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 90
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:23:30.309926Z",
     "start_time": "2024-09-18T13:23:11.445568Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# knn 模型\n",
    "stdScaler = StandardScaler()\n",
    "X = stdScaler.fit_transform(train_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "model_clf(clf)"
   ],
   "id": "48b7bcb0ce0897c8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9062389176170238\n",
      "Test AUC Score 0.5210002172870257\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 8.27554246e-05, 2.53231599e-03, 3.40290306e-02,\n",
       "        2.64287724e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 7.75795190e-04, 3.10318076e-03, 4.26687355e-02,\n",
       "        3.05404706e-01, 1.00000000e+00]))"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:23:47.173877Z",
     "start_time": "2024-09-18T13:23:46.831383Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# GaussianNB 模型\n",
    "stdScaler = StandardScaler()\n",
    "X = stdScaler.fit_transform(train_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)\n",
    "clf = GaussianNB()\n",
    "model_clf(clf)"
   ],
   "id": "d2aeb7df3d2875b5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5618845548568886\n",
      "Test AUC Score 0.5570794423784978\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.00532945, 0.00536255, ..., 0.99970208, 0.99970208,\n",
       "        1.        ]),\n",
       " array([0.        , 0.00930954, 0.00930954, ..., 0.9997414 , 1.        ,\n",
       "        1.        ]))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:24:06.755318Z",
     "start_time": "2024-09-18T13:24:01.815352Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 决策树模型\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "model_clf(clf)"
   ],
   "id": "ea5b012f215adeea",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9979673944771675\n",
      "Test AUC Score 0.5082171064746188\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.06430096, 0.06473129, 0.08108377, 0.08110032,\n",
       "        0.08601599, 0.08690975, 0.08714146, 0.08727387, 0.08738973,\n",
       "        0.08740628, 0.08747248, 1.        ]),\n",
       " array([0.        , 0.07551073, 0.07628653, 0.097233  , 0.097233  ,\n",
       "        0.10240497, 0.10343936, 0.10369796, 0.10369796, 0.10395656,\n",
       "        0.10395656, 0.10395656, 1.        ]))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 93
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:24:53.765825Z",
     "start_time": "2024-09-18T13:24:20.869337Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# bagging模型\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = BaggingClassifier(KNeighborsClassifier(), max_samples=0.5, max_features=0.5)\n",
    "model_clf(clf)"
   ],
   "id": "1e1b141fa6666fd8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9163550101307664\n",
      "Test AUC Score 0.5386085600062622\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 4.96532548e-05, 6.62043397e-05, 1.65510849e-04,\n",
       "        2.64817359e-04, 3.14470614e-04, 3.97226038e-04, 5.46185802e-04,\n",
       "        5.62736887e-04, 5.79287972e-04, 7.94452076e-04, 1.00961618e-03,\n",
       "        1.02616727e-03, 1.14202486e-03, 1.50614873e-03, 1.91992585e-03,\n",
       "        1.95302802e-03, 2.25094755e-03, 3.21091048e-03, 3.44262566e-03,\n",
       "        4.76671246e-03, 6.27286119e-03, 6.45492312e-03, 9.36791407e-03,\n",
       "        1.06920009e-02, 1.13374932e-02, 1.65841871e-02, 1.84213575e-02,\n",
       "        2.02088747e-02, 3.15298168e-02, 4.51844618e-02, 5.32944935e-02,\n",
       "        6.23644880e-02, 8.73400751e-02, 1.08972343e-01, 1.43100680e-01,\n",
       "        2.30324898e-01, 3.51329880e-01, 5.02341979e-01, 5.10882338e-01,\n",
       "        6.96784124e-01, 8.76528906e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.58598397e-04, 2.58598397e-04, 7.75795190e-04, 1.55159038e-03,\n",
       "        1.55159038e-03, 1.81018878e-03, 2.84458236e-03, 3.87897595e-03,\n",
       "        4.13757435e-03, 4.39617274e-03, 6.20636152e-03, 6.72355831e-03,\n",
       "        9.30954228e-03, 1.13783295e-02, 1.18955262e-02, 1.68088958e-02,\n",
       "        2.04292733e-02, 2.19808637e-02, 3.07732092e-02, 3.25833980e-02,\n",
       "        3.49107836e-02, 5.09438841e-02, 7.00801655e-02, 7.91311094e-02,\n",
       "        8.97336437e-02, 1.19472459e-01, 1.45073701e-01, 1.86449444e-01,\n",
       "        2.79027670e-01, 4.01086113e-01, 5.57020946e-01, 5.63485906e-01,\n",
       "        7.40884407e-01, 8.98888027e-01, 1.00000000e+00]))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 94
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:24:54.771591Z",
     "start_time": "2024-09-18T13:24:53.767844Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 随机森林\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = RandomForestClassifier(n_estimators=10, max_depth=3, min_samples_split=12, random_state=0)\n",
    "model_clf(clf)"
   ],
   "id": "81ceef933979d9a9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5758178585854464\n",
      "Test AUC Score 0.5691249577507556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.65510849e-05, 3.31021698e-05, ...,\n",
       "        9.94405733e-01, 9.98857975e-01, 1.00000000e+00]),\n",
       " array([0.        , 0.        , 0.        , ..., 0.99818981, 0.9997414 ,\n",
       "        1.        ]))"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 95
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:25:00.505708Z",
     "start_time": "2024-09-18T13:24:57.056406Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# ExTree模型\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)\n",
    "model_clf(clf)"
   ],
   "id": "9a7866aa333225f6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9979673944771675\n",
      "Test AUC Score 0.5275002910136131\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.01317466, 0.01334017, 0.01338983, 0.01347258,\n",
       "        0.01353879, 0.01949718, 0.01953028, 0.01953028, 0.01974544,\n",
       "        0.019762  , 0.02282395, 0.0229067 , 0.02297291, 0.02490938,\n",
       "        0.02730929, 0.02754101, 0.02764031, 0.02768997, 0.02773962,\n",
       "        0.04324798, 0.04326454, 0.04357901, 0.04367831, 0.04369486,\n",
       "        0.04399278, 0.04404244, 0.05289727, 0.05337725, 0.05458548,\n",
       "        0.05501581, 0.05506546, 0.05509856, 0.05526407, 0.05526407,\n",
       "        0.05529717, 0.12125325, 0.12146841, 0.12148496, 0.12242838,\n",
       "        0.12264354, 0.12267664, 0.12272629, 0.1228256 , 0.12358695,\n",
       "        0.12362005, 0.12366971, 0.12368626, 0.14831427, 0.14834737,\n",
       "        0.14846323, 0.14849633, 0.15384233, 0.15491815, 0.15505056,\n",
       "        0.15511677, 0.15541469, 0.15543124, 0.15546434, 0.15548089,\n",
       "        0.35839719, 0.35841374, 0.35844685, 0.3584634 , 0.35995299,\n",
       "        0.3600192 , 0.36035022, 0.36046608, 0.36193913, 0.36205498,\n",
       "        0.36208808, 0.40313478, 0.40315133, 0.41200616, 0.41361161,\n",
       "        0.41379367, 0.41387643, 0.41397574, 1.        ]),\n",
       " array([0.        , 0.01991208, 0.02068787, 0.02068787, 0.02068787,\n",
       "        0.02068787, 0.02922162, 0.02922162, 0.02948022, 0.02999741,\n",
       "        0.02999741, 0.032842  , 0.032842  , 0.032842  , 0.03413499,\n",
       "        0.03749677, 0.03775537, 0.03775537, 0.03775537, 0.03775537,\n",
       "        0.06180502, 0.06180502, 0.06180502, 0.06206362, 0.06206362,\n",
       "        0.06206362, 0.06206362, 0.06852858, 0.06904577, 0.07033876,\n",
       "        0.07137316, 0.07137316, 0.07137316, 0.07137316, 0.07163176,\n",
       "        0.07163176, 0.14998707, 0.14998707, 0.14998707, 0.15205586,\n",
       "        0.15205586, 0.15205586, 0.15205586, 0.15205586, 0.15360745,\n",
       "        0.15360745, 0.15360745, 0.15360745, 0.17869149, 0.17895009,\n",
       "        0.17920869, 0.17920869, 0.18696664, 0.18800103, 0.18877683,\n",
       "        0.18877683, 0.18955262, 0.18955262, 0.18955262, 0.18955262,\n",
       "        0.40418929, 0.40418929, 0.40418929, 0.40418929, 0.40625808,\n",
       "        0.40625808, 0.40625808, 0.40625808, 0.40780967, 0.40806827,\n",
       "        0.40806827, 0.44840962, 0.44840962, 0.45797776, 0.46004655,\n",
       "        0.46082234, 0.46082234, 0.46082234, 1.        ]))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 96
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:25:15.177464Z",
     "start_time": "2024-09-18T13:25:12.361648Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Adaboost\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = AdaBoostClassifier(n_estimators=10)\n",
    "model_clf(clf)"
   ],
   "id": "42174c801fd53917",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5836438805860764\n",
      "Test AUC Score 0.5730243325815666\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 8.27554246e-05, 3.47572783e-04, 1.70476175e-03,\n",
       "        3.02884854e-03, 3.17780831e-03, 5.14738741e-03, 8.97068803e-03,\n",
       "        1.58559394e-02, 2.07054072e-02, 2.16984723e-02, 2.21950049e-02,\n",
       "        2.48100763e-02, 2.58527947e-02, 5.88722091e-02, 6.81408166e-02,\n",
       "        6.81739188e-02, 7.18482597e-02, 7.64329102e-02, 7.75583840e-02,\n",
       "        7.82535295e-02, 8.28878333e-02, 8.64959698e-02, 8.65125209e-02,\n",
       "        8.98061868e-02, 9.55825154e-02, 1.06208312e-01, 1.98530264e-01,\n",
       "        2.02866648e-01, 2.03048710e-01, 2.06905113e-01, 2.10695311e-01,\n",
       "        2.11870438e-01, 2.12019398e-01, 2.12184909e-01, 2.30953839e-01,\n",
       "        2.45998775e-01, 2.46247041e-01, 2.49176584e-01, 2.50417915e-01,\n",
       "        2.53512968e-01, 2.59338950e-01, 2.59802380e-01, 2.82858041e-01,\n",
       "        3.02239362e-01, 3.05019944e-01, 3.05036495e-01, 3.06708155e-01,\n",
       "        3.07403300e-01, 3.11292805e-01, 3.12881709e-01, 3.12947914e-01,\n",
       "        3.13129976e-01, 3.13146527e-01, 3.26784621e-01, 3.54772505e-01,\n",
       "        3.70396730e-01, 3.70611894e-01, 3.70727751e-01, 3.74484847e-01,\n",
       "        3.83058309e-01, 3.85673381e-01, 3.85689932e-01, 3.99857661e-01,\n",
       "        4.14703984e-01, 4.14770188e-01, 4.19536901e-01, 4.19669309e-01,\n",
       "        4.19917576e-01, 4.19967229e-01, 4.28457935e-01, 4.39811980e-01,\n",
       "        4.39861633e-01, 4.40027144e-01, 4.43304259e-01, 4.45985534e-01,\n",
       "        4.47028253e-01, 4.48203380e-01, 4.50785349e-01, 4.51132922e-01,\n",
       "        4.52142538e-01, 4.52192191e-01, 4.72202453e-01, 5.19389596e-01,\n",
       "        5.24255615e-01, 5.48751221e-01, 5.75348814e-01, 5.76739105e-01,\n",
       "        5.77699068e-01, 6.13962495e-01, 6.16627220e-01, 6.16825833e-01,\n",
       "        6.16875486e-01, 6.17206508e-01, 6.17223059e-01, 6.22386997e-01,\n",
       "        6.22734570e-01, 6.25945481e-01, 6.25962032e-01, 6.26011685e-01,\n",
       "        6.26822688e-01, 6.29686026e-01, 6.29719128e-01, 6.31589401e-01,\n",
       "        6.31870769e-01, 6.31920422e-01, 6.32152138e-01, 6.42182095e-01,\n",
       "        6.51070028e-01, 6.51103130e-01, 6.75830451e-01, 6.75863553e-01,\n",
       "        6.83311541e-01, 6.84999752e-01, 6.87532068e-01, 6.87565170e-01,\n",
       "        6.87631374e-01, 6.99812973e-01, 7.56318377e-01, 7.56516990e-01,\n",
       "        7.57195584e-01, 7.57410748e-01, 7.57476953e-01, 7.73465301e-01,\n",
       "        7.73912180e-01, 7.74276304e-01, 8.70454658e-01, 8.70487760e-01,\n",
       "        8.70520863e-01, 8.70537414e-01, 8.72258727e-01, 8.72324931e-01,\n",
       "        8.72705606e-01, 8.72804912e-01, 8.72821463e-01, 8.86327149e-01,\n",
       "        9.00643837e-01, 9.01719658e-01, 9.02149986e-01, 9.02365150e-01,\n",
       "        9.03093398e-01, 9.03722339e-01, 9.03805094e-01, 9.22590576e-01,\n",
       "        9.26480081e-01, 9.29955809e-01, 9.30005462e-01, 9.30121319e-01,\n",
       "        9.32157103e-01, 9.32239858e-01, 9.32256409e-01, 9.32438471e-01,\n",
       "        9.32504676e-01, 9.32554329e-01, 9.56073421e-01, 9.56089972e-01,\n",
       "        9.56735464e-01, 9.64961353e-01, 9.67758487e-01, 9.67775038e-01,\n",
       "        9.67857793e-01, 9.68023304e-01, 9.69430146e-01, 9.70406660e-01,\n",
       "        9.72177626e-01, 9.72839670e-01, 9.73021732e-01, 9.73054834e-01,\n",
       "        9.73104487e-01, 9.73253447e-01, 9.73799633e-01, 9.73849286e-01,\n",
       "        9.74809249e-01, 9.77142952e-01, 9.77209156e-01, 9.77358116e-01,\n",
       "        9.80105596e-01, 9.80204902e-01, 9.91939622e-01, 9.91956173e-01,\n",
       "        9.92254092e-01, 9.92353399e-01, 9.92403052e-01, 9.92568563e-01,\n",
       "        9.92849931e-01, 9.92866482e-01, 9.92899585e-01, 9.94091263e-01,\n",
       "        9.94852613e-01, 9.94885715e-01, 9.94951919e-01, 9.98013870e-01,\n",
       "        9.98080074e-01, 9.98129727e-01, 9.98874526e-01, 9.98907628e-01,\n",
       "        9.99139344e-01, 9.99751734e-01, 9.99867591e-01, 9.99983449e-01,\n",
       "        1.00000000e+00]),\n",
       " array([0.00000000e+00, 2.58598397e-04, 1.03439359e-03, 4.65477114e-03,\n",
       "        6.98215671e-03, 6.98215671e-03, 1.03439359e-02, 2.01706749e-02,\n",
       "        2.94802172e-02, 3.74967675e-02, 4.03413499e-02, 4.08585467e-02,\n",
       "        4.52547194e-02, 4.68063098e-02, 9.72329972e-02, 1.14300491e-01,\n",
       "        1.14559090e-01, 1.19472459e-01, 1.25161624e-01, 1.26971813e-01,\n",
       "        1.26971813e-01, 1.37574347e-01, 1.43780709e-01, 1.43780709e-01,\n",
       "        1.46366693e-01, 1.55417636e-01, 1.68088958e-01, 2.88854409e-01,\n",
       "        2.94543574e-01, 2.95060771e-01, 3.01008534e-01, 3.05663305e-01,\n",
       "        3.06956297e-01, 3.06956297e-01, 3.07214895e-01, 3.32816137e-01,\n",
       "        3.45746056e-01, 3.46521852e-01, 3.49625032e-01, 3.50918024e-01,\n",
       "        3.53245410e-01, 3.57382984e-01, 3.57641583e-01, 3.83760021e-01,\n",
       "        4.03930696e-01, 4.05223688e-01, 4.05223688e-01, 4.06258081e-01,\n",
       "        4.06516680e-01, 4.12723041e-01, 4.16343419e-01, 4.16343419e-01,\n",
       "        4.16602017e-01, 4.16602017e-01, 4.29273339e-01, 4.56167572e-01,\n",
       "        4.73493664e-01, 4.73493664e-01, 4.73752263e-01, 4.77889837e-01,\n",
       "        4.88750970e-01, 4.90302560e-01, 4.90302560e-01, 5.01939488e-01,\n",
       "        5.22885958e-01, 5.22885958e-01, 5.25213344e-01, 5.25471942e-01,\n",
       "        5.25730540e-01, 5.25730540e-01, 5.33747091e-01, 5.46677011e-01,\n",
       "        5.46677011e-01, 5.46677011e-01, 5.50555987e-01, 5.51590380e-01,\n",
       "        5.52107577e-01, 5.53917766e-01, 5.54693561e-01, 5.55469356e-01,\n",
       "        5.56245151e-01, 5.56245151e-01, 5.74605637e-01, 6.20636152e-01,\n",
       "        6.25808120e-01, 6.48047582e-01, 6.68476855e-01, 6.68994052e-01,\n",
       "        6.68994052e-01, 7.07266615e-01, 7.09594001e-01, 7.10369796e-01,\n",
       "        7.10369796e-01, 7.10628394e-01, 7.10628394e-01, 7.15024567e-01,\n",
       "        7.15541764e-01, 7.18127748e-01, 7.18127748e-01, 7.18127748e-01,\n",
       "        7.18644944e-01, 7.23041117e-01, 7.23299716e-01, 7.24592708e-01,\n",
       "        7.24851306e-01, 7.24851306e-01, 7.25368503e-01, 7.35971037e-01,\n",
       "        7.42694595e-01, 7.42694595e-01, 7.62348073e-01, 7.62348073e-01,\n",
       "        7.68295837e-01, 7.68813033e-01, 7.70364624e-01, 7.70364624e-01,\n",
       "        7.70364624e-01, 7.80967158e-01, 8.24928885e-01, 8.25187484e-01,\n",
       "        8.25704681e-01, 8.26221877e-01, 8.26480476e-01, 8.37341608e-01,\n",
       "        8.37600207e-01, 8.37600207e-01, 9.06645979e-01, 9.06904577e-01,\n",
       "        9.06904577e-01, 9.06904577e-01, 9.08973364e-01, 9.08973364e-01,\n",
       "        9.09490561e-01, 9.09490561e-01, 9.09490561e-01, 9.17507111e-01,\n",
       "        9.30695630e-01, 9.31212826e-01, 9.31471425e-01, 9.31730023e-01,\n",
       "        9.32505818e-01, 9.33281614e-01, 9.33281614e-01, 9.48797517e-01,\n",
       "        9.50090509e-01, 9.51900698e-01, 9.51900698e-01, 9.51900698e-01,\n",
       "        9.52935092e-01, 9.52935092e-01, 9.52935092e-01, 9.53193690e-01,\n",
       "        9.53193690e-01, 9.53193690e-01, 9.70519783e-01, 9.70519783e-01,\n",
       "        9.71295578e-01, 9.77243341e-01, 9.79570727e-01, 9.79570727e-01,\n",
       "        9.79829325e-01, 9.79829325e-01, 9.80863719e-01, 9.81639514e-01,\n",
       "        9.82415309e-01, 9.82932506e-01, 9.83191104e-01, 9.83191104e-01,\n",
       "        9.83449703e-01, 9.83708301e-01, 9.84225498e-01, 9.84225498e-01,\n",
       "        9.84484096e-01, 9.85259891e-01, 9.85518490e-01, 9.85518490e-01,\n",
       "        9.87845875e-01, 9.87845875e-01, 9.95862426e-01, 9.95862426e-01,\n",
       "        9.95862426e-01, 9.95862426e-01, 9.95862426e-01, 9.96379622e-01,\n",
       "        9.96379622e-01, 9.96379622e-01, 9.96379622e-01, 9.96638221e-01,\n",
       "        9.96896819e-01, 9.96896819e-01, 9.96896819e-01, 9.98448410e-01,\n",
       "        9.98448410e-01, 9.98448410e-01, 9.99224205e-01, 9.99224205e-01,\n",
       "        9.99224205e-01, 9.99741402e-01, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00]))"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 97
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:25:32.303347Z",
     "start_time": "2024-09-18T13:25:30.145930Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# GBDT\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)\n",
    "model_clf(clf)"
   ],
   "id": "b101d2ef0eea8f9c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5835221995665941\n",
      "Test AUC Score 0.5766901539273581\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 6.62043397e-05, 9.93065095e-05, 6.62043397e-04,\n",
       "        9.93065095e-04, 1.10892269e-03, 1.34394810e-02, 1.36546451e-02,\n",
       "        1.36711961e-02, 1.42670352e-02, 1.57731839e-02, 1.59552459e-02,\n",
       "        1.96130356e-02, 1.99771595e-02, 2.01592214e-02, 2.06557540e-02,\n",
       "        2.23439646e-02, 3.96563995e-02, 3.97391549e-02, 4.64092421e-02,\n",
       "        5.01663384e-02, 5.25331435e-02, 5.28476142e-02, 5.28641652e-02,\n",
       "        5.39565369e-02, 5.43703140e-02, 5.47509889e-02, 5.57109519e-02,\n",
       "        5.94349460e-02, 6.26127543e-02, 1.80473030e-01, 1.82525365e-01,\n",
       "        1.87854814e-01, 1.87887916e-01, 1.88119631e-01, 1.93300121e-01,\n",
       "        1.94111124e-01, 1.94226982e-01, 2.05299657e-01, 2.05779639e-01,\n",
       "        2.10447045e-01, 2.18043993e-01, 2.18143299e-01, 2.25955411e-01,\n",
       "        2.31483474e-01, 2.32128966e-01, 2.36647412e-01, 2.41943759e-01,\n",
       "        2.42158924e-01, 2.94460352e-01, 2.97803671e-01, 2.98780185e-01,\n",
       "        2.98978798e-01, 3.13312038e-01, 3.14222347e-01, 3.19601450e-01,\n",
       "        3.22762707e-01, 3.73690395e-01, 3.75808934e-01, 3.86815406e-01,\n",
       "        3.86831957e-01, 4.09738658e-01, 4.16259786e-01, 4.16640461e-01,\n",
       "        4.33340505e-01, 4.35094920e-01, 4.66442675e-01, 4.73609295e-01,\n",
       "        4.74023072e-01, 4.75429914e-01, 4.90623810e-01, 4.90690015e-01,\n",
       "        4.99081415e-01, 5.07754183e-01, 5.09972029e-01, 5.12388487e-01,\n",
       "        5.13580165e-01, 5.58897036e-01, 6.00705076e-01, 6.03038779e-01,\n",
       "        6.03386352e-01, 6.46468826e-01, 6.50606597e-01, 6.64211589e-01,\n",
       "        7.02477697e-01, 7.04827951e-01, 7.05556199e-01, 7.07972658e-01,\n",
       "        7.07989209e-01, 7.08816763e-01, 7.09065029e-01, 7.36423973e-01,\n",
       "        7.37681855e-01, 7.37714957e-01, 7.52875751e-01, 7.54117082e-01,\n",
       "        7.59860309e-01, 7.84620732e-01, 7.84670385e-01, 7.86590311e-01,\n",
       "        8.40348235e-01, 9.32802595e-01, 9.32968106e-01, 9.47119284e-01,\n",
       "        9.47566163e-01, 9.47880634e-01, 9.55957563e-01, 9.57778182e-01,\n",
       "        9.69910128e-01, 9.70390109e-01, 9.70439762e-01, 9.72823119e-01,\n",
       "        9.75703007e-01, 9.94670551e-01, 9.95514656e-01, 9.97087009e-01,\n",
       "        9.99271752e-01, 9.99751734e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 5.17196793e-04, 1.03439359e-03, 2.84458236e-03,\n",
       "        3.87897595e-03, 4.39617274e-03, 2.63770365e-02, 2.66356349e-02,\n",
       "        2.66356349e-02, 2.76700284e-02, 3.20662012e-02, 3.23247996e-02,\n",
       "        4.03413499e-02, 4.16343419e-02, 4.18929403e-02, 4.37031290e-02,\n",
       "        4.52547194e-02, 6.98215671e-02, 6.98215671e-02, 8.43030773e-02,\n",
       "        8.92164469e-02, 9.23196276e-02, 9.36126196e-02, 9.36126196e-02,\n",
       "        9.61986036e-02, 9.61986036e-02, 9.69743988e-02, 9.93017843e-02,\n",
       "        1.03956555e-01, 1.08611327e-01, 2.61184381e-01, 2.63511766e-01,\n",
       "        2.71528317e-01, 2.71528317e-01, 2.72045513e-01, 2.78510473e-01,\n",
       "        2.80062064e-01, 2.80579260e-01, 2.92733385e-01, 2.93250582e-01,\n",
       "        3.01267132e-01, 3.08507887e-01, 3.09025084e-01, 3.19369020e-01,\n",
       "        3.26609775e-01, 3.27644169e-01, 3.34109129e-01, 3.39022498e-01,\n",
       "        3.39798293e-01, 3.99017326e-01, 4.00051720e-01, 4.01086113e-01,\n",
       "        4.01086113e-01, 4.16602017e-01, 4.18153607e-01, 4.25911559e-01,\n",
       "        4.30824929e-01, 4.86164986e-01, 4.87716576e-01, 4.95215930e-01,\n",
       "        4.95474528e-01, 5.23144557e-01, 5.28833721e-01, 5.29092320e-01,\n",
       "        5.45125420e-01, 5.46935609e-01, 5.78484613e-01, 5.83397983e-01,\n",
       "        5.83656581e-01, 5.84173778e-01, 5.97103698e-01, 5.97362296e-01,\n",
       "        6.06671839e-01, 6.13136799e-01, 6.14171192e-01, 6.16498578e-01,\n",
       "        6.17532971e-01, 6.57874321e-01, 6.92785105e-01, 6.94595294e-01,\n",
       "        6.94595294e-01, 7.37005431e-01, 7.39332816e-01, 7.50969744e-01,\n",
       "        7.86139126e-01, 7.88725110e-01, 7.90018102e-01, 7.92345487e-01,\n",
       "        7.92345487e-01, 7.92604086e-01, 7.92862684e-01, 8.16395138e-01,\n",
       "        8.17429532e-01, 8.17429532e-01, 8.29325058e-01, 8.30359452e-01,\n",
       "        8.34238428e-01, 8.56995087e-01, 8.57253685e-01, 8.57512283e-01,\n",
       "        9.00181019e-01, 9.62503232e-01, 9.62761831e-01, 9.70519783e-01,\n",
       "        9.70519783e-01, 9.70778381e-01, 9.75174554e-01, 9.76726144e-01,\n",
       "        9.83449703e-01, 9.83708301e-01, 9.83708301e-01, 9.84484096e-01,\n",
       "        9.85518490e-01, 9.97155418e-01, 9.97672614e-01, 9.99224205e-01,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 98
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:27:37.112999Z",
     "start_time": "2024-09-18T13:27:37.026480Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# lgb\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, test_size=0.4, random_state=0)\n",
    "X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)\n",
    "clf = lightgbm\n",
    "train_matrix = clf.Dataset(X_train, label=y_train)\n",
    "test_matrix = clf.Dataset(X_test, label=y_test)\n",
    "params = {\n",
    "          'boosting_type': 'gbdt',\n",
    "          #'boosting_type': 'dart',\n",
    "          'objective': 'multiclass',\n",
    "          'metric': 'multi_logloss',\n",
    "          'min_child_weight': 1.5,\n",
    "          'num_leaves': 2**5,\n",
    "          'lambda_l2': 10,\n",
    "          'subsample': 0.7,\n",
    "          'colsample_bytree': 0.7,\n",
    "          'colsample_bylevel': 0.7,\n",
    "          'learning_rate': 0.03,\n",
    "          'tree_method': 'exact',\n",
    "          'seed': 2017,\n",
    "          \"num_class\": 2,\n",
    "          'silent': True,\n",
    "          }\n",
    "num_round = 10000\n",
    "early_stopping_rounds = 100\n",
    "model = clf.train(params, \n",
    "                  train_matrix,\n",
    "                  num_round,\n",
    "                  valid_sets=test_matrix,\n",
    "                  early_stopping_rounds=early_stopping_rounds)\n",
    "\n",
    "y_train_pred = model.predict(X_train,num_iteration=model.best_iteration)\n",
    "y_train_pred_pos = y_train_pred[:,1]\n",
    "\n",
    "y_test_pred = model.predict(X_valid,num_iteration=model.best_iteration)\n",
    "y_test_pred_pos = y_test_pred[:,1]\n",
    "\n",
    "auc_train = roc_auc_score(y_train, y_train_pred_pos)#AUC评分\n",
    "\n",
    "auc_test = roc_auc_score(y_valid, y_test_pred_pos)\n",
    "\n",
    "print(f\"Train AUC Score {auc_train}\")\n",
    "print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_valid,y_test_pred_pos)#绘制ROC曲线\n"
   ],
   "id": "66c3f2f253cd7094",
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "train() got an unexpected keyword argument 'early_stopping_rounds'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_27472\\1878424434.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     24\u001B[0m \u001B[0mnum_round\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;36m10000\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     25\u001B[0m \u001B[0mearly_stopping_rounds\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;36m100\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 26\u001B[1;33m model = clf.train(params, \n\u001B[0m\u001B[0;32m     27\u001B[0m                   \u001B[0mtrain_matrix\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     28\u001B[0m                   \u001B[0mnum_round\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mTypeError\u001B[0m: train() got an unexpected keyword argument 'early_stopping_rounds'"
     ]
    }
   ],
   "execution_count": 102
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:29:17.697796Z",
     "start_time": "2024-09-18T13:29:17.689688Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_auc_curve(fpr, tpr, auc):\n",
    "    plt.figure(figsize = (16,6))\n",
    "    plt.plot(fpr,tpr,'b+',linestyle = '-')\n",
    "    plt.fill_between(fpr, tpr, alpha = 0.5)\n",
    "    plt.ylabel('True Postive Rate')\n",
    "    plt.xlabel('False Postive Rate')\n",
    "    plt.title(f'ROC Curve Having AUC = {auc}')"
   ],
   "id": "606b13a9f33e36a9",
   "outputs": [],
   "execution_count": 103
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:29:23.491469Z",
     "start_time": "2024-09-18T13:29:23.474638Z"
    }
   },
   "cell_type": "code",
   "source": "plot_auc_curve(fpr, tpr, auc_test)",
   "id": "567fd56e688a0880",
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fpr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_27472\\778955626.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mplot_auc_curve\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mauc_test\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m: name 'fpr' is not defined"
     ]
    }
   ],
   "execution_count": 104
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:30:34.087119Z",
     "start_time": "2024-09-18T13:30:08.789870Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import xgboost\n",
    "\n",
    "# xgb\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, test_size=0.4, random_state=0)\n",
    "X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)\n",
    "\n",
    "clf = xgboost\n",
    "\n",
    "train_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)\n",
    "test_matrix = clf.DMatrix(X_test, label=y_test, missing=-1)\n",
    "z = clf.DMatrix(X_valid, label=y_valid, missing=-1)\n",
    "params = {'booster': 'gbtree',\n",
    "          'objective': 'multi:softprob',\n",
    "          'eval_metric': 'mlogloss',\n",
    "          'gamma': 1,\n",
    "          'min_child_weight': 1.5,\n",
    "          'max_depth': 5,\n",
    "          'lambda': 100,\n",
    "          'subsample': 0.7,\n",
    "          'colsample_bytree': 0.7,\n",
    "          'colsample_bylevel': 0.7,\n",
    "          'eta': 0.03,\n",
    "          'tree_method': 'exact',\n",
    "          'seed': 2017,\n",
    "          \"num_class\": 2\n",
    "          }\n",
    "score_dict={}\n",
    "num_round = 10000\n",
    "early_stopping_rounds = 100\n",
    "watchlist = [(train_matrix, 'train'),\n",
    "             (test_matrix, 'eval')\n",
    "             ]\n",
    "model = clf.train(params,\n",
    "                  train_matrix,\n",
    "                  num_boost_round=num_round,#训练的轮数，即生成的树的数量\n",
    "                  evals=watchlist,#训练时用于评估模型的数据集合，用户可以通过验证集来评估模型好坏\n",
    "                  early_stopping_rounds=early_stopping_rounds,\n",
    "                  #激活早停止，当验证的错误率early_stopping_rounds轮未下降，则停止训练。\n",
    "                  #如果发生了早停止，则模型会提供三个额外的字段：bst.best_score, bst.best_iteration 和 bst.best_ntree_limit\n",
    "                  evals_result =score_dict\n",
    "                  )\n",
    "y_train_pred = model.predict(train_matrix ,ntree_limit=model.best_iteration)#ntree_limit  用于预测的树的数量\n",
    "y_train_pred_pos = y_train_pred[:,1]\n",
    "\n",
    "y_test_pred = model.predict(z,ntree_limit=model.best_iteration)\n",
    "y_test_pred_pos = y_test_pred[:,1]\n",
    "\n",
    "auc_train = roc_auc_score(y_train, y_train_pred_pos)#AUC评分\n",
    "\n",
    "auc_test = roc_auc_score(y_valid, y_test_pred_pos)\n",
    "\n",
    "print(f\"Train AUC Score {auc_train}\")\n",
    "print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_valid,y_test_pred_pos)#绘制ROC曲线"
   ],
   "id": "152d95e61bd1cb54",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mlogloss:0.67057\teval-mlogloss:0.67051\n",
      "[1]\ttrain-mlogloss:0.64930\teval-mlogloss:0.64918\n",
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      "[455]\ttrain-mlogloss:0.22432\teval-mlogloss:0.22779\n",
      "[456]\ttrain-mlogloss:0.22431\teval-mlogloss:0.22778\n",
      "[457]\ttrain-mlogloss:0.22430\teval-mlogloss:0.22778\n",
      "[458]\ttrain-mlogloss:0.22429\teval-mlogloss:0.22778\n",
      "[459]\ttrain-mlogloss:0.22428\teval-mlogloss:0.22778\n",
      "[460]\ttrain-mlogloss:0.22427\teval-mlogloss:0.22778\n",
      "[461]\ttrain-mlogloss:0.22426\teval-mlogloss:0.22778\n",
      "[462]\ttrain-mlogloss:0.22425\teval-mlogloss:0.22779\n",
      "[463]\ttrain-mlogloss:0.22424\teval-mlogloss:0.22779\n",
      "[464]\ttrain-mlogloss:0.22422\teval-mlogloss:0.22778\n",
      "[465]\ttrain-mlogloss:0.22421\teval-mlogloss:0.22779\n",
      "[466]\ttrain-mlogloss:0.22420\teval-mlogloss:0.22778\n",
      "[467]\ttrain-mlogloss:0.22419\teval-mlogloss:0.22778\n",
      "[468]\ttrain-mlogloss:0.22418\teval-mlogloss:0.22779\n",
      "[469]\ttrain-mlogloss:0.22417\teval-mlogloss:0.22779\n",
      "[470]\ttrain-mlogloss:0.22416\teval-mlogloss:0.22779\n",
      "[471]\ttrain-mlogloss:0.22415\teval-mlogloss:0.22779\n",
      "[472]\ttrain-mlogloss:0.22414\teval-mlogloss:0.22779\n",
      "[473]\ttrain-mlogloss:0.22413\teval-mlogloss:0.22779\n",
      "[474]\ttrain-mlogloss:0.22412\teval-mlogloss:0.22779\n",
      "[475]\ttrain-mlogloss:0.22411\teval-mlogloss:0.22779\n",
      "[476]\ttrain-mlogloss:0.22411\teval-mlogloss:0.22779\n",
      "[477]\ttrain-mlogloss:0.22409\teval-mlogloss:0.22780\n",
      "[478]\ttrain-mlogloss:0.22408\teval-mlogloss:0.22780\n",
      "[479]\ttrain-mlogloss:0.22407\teval-mlogloss:0.22780\n",
      "[480]\ttrain-mlogloss:0.22407\teval-mlogloss:0.22780\n",
      "[481]\ttrain-mlogloss:0.22405\teval-mlogloss:0.22780\n",
      "[482]\ttrain-mlogloss:0.22404\teval-mlogloss:0.22780\n",
      "[483]\ttrain-mlogloss:0.22403\teval-mlogloss:0.22780\n",
      "[484]\ttrain-mlogloss:0.22402\teval-mlogloss:0.22780\n",
      "[485]\ttrain-mlogloss:0.22401\teval-mlogloss:0.22781\n",
      "[486]\ttrain-mlogloss:0.22400\teval-mlogloss:0.22781\n",
      "[487]\ttrain-mlogloss:0.22399\teval-mlogloss:0.22780\n",
      "[488]\ttrain-mlogloss:0.22398\teval-mlogloss:0.22780\n",
      "[489]\ttrain-mlogloss:0.22397\teval-mlogloss:0.22781\n",
      "[490]\ttrain-mlogloss:0.22396\teval-mlogloss:0.22780\n",
      "[491]\ttrain-mlogloss:0.22394\teval-mlogloss:0.22781\n",
      "[492]\ttrain-mlogloss:0.22394\teval-mlogloss:0.22780\n",
      "[493]\ttrain-mlogloss:0.22393\teval-mlogloss:0.22780\n",
      "[494]\ttrain-mlogloss:0.22392\teval-mlogloss:0.22780\n",
      "[495]\ttrain-mlogloss:0.22391\teval-mlogloss:0.22780\n",
      "[496]\ttrain-mlogloss:0.22390\teval-mlogloss:0.22780\n",
      "[497]\ttrain-mlogloss:0.22389\teval-mlogloss:0.22780\n",
      "[498]\ttrain-mlogloss:0.22388\teval-mlogloss:0.22780\n",
      "[499]\ttrain-mlogloss:0.22387\teval-mlogloss:0.22780\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "predict() got an unexpected keyword argument 'ntree_limit'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_27472\\1896354288.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     40\u001B[0m                   \u001B[0mevals_result\u001B[0m \u001B[1;33m=\u001B[0m\u001B[0mscore_dict\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     41\u001B[0m                   )\n\u001B[1;32m---> 42\u001B[1;33m \u001B[0my_train_pred\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtrain_matrix\u001B[0m \u001B[1;33m,\u001B[0m\u001B[0mntree_limit\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbest_iteration\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;31m#ntree_limit  用于预测的树的数量\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     43\u001B[0m \u001B[0my_train_pred_pos\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0my_train_pred\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     44\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mTypeError\u001B[0m: predict() got an unexpected keyword argument 'ntree_limit'"
     ]
    }
   ],
   "execution_count": 105
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:31:04.973683Z",
     "start_time": "2024-09-18T13:31:04.962556Z"
    }
   },
   "cell_type": "code",
   "source": "plot_auc_curve(fpr, tpr, auc_test)",
   "id": "da19013ee2b707a6",
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fpr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_27472\\778955626.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mplot_auc_curve\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mauc_test\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m: name 'fpr' is not defined"
     ]
    }
   ],
   "execution_count": 106
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:31:14.063384Z",
     "start_time": "2024-09-18T13:31:13.950374Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "plt.plot(score_dict['train']['mlogloss'], 'r-+', label = 'Training Loss')\n",
    "plt.plot(score_dict['eval']['mlogloss'], 'b-', label = 'Test Loss')"
   ],
   "id": "514421e72a7ee663",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6c61879a0>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 107
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:34:13.143572Z",
     "start_time": "2024-09-18T13:34:13.080582Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# xgb调参\n",
    "features_columns = [col for col in train_data.columns if col not in ['user_id','label']]\n",
    "train_data_1 = train_data[features_columns]\n",
    "test_data_1 = test_data[features_columns]\n",
    "target =train_data['label']"
   ],
   "id": "aa63f735f15eab59",
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'label'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001B[0m in \u001B[0;36mget_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m   3801\u001B[0m             \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3802\u001B[1;33m                 \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_engine\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_loc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcasted_key\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3803\u001B[0m             \u001B[1;32mexcept\u001B[0m \u001B[0mKeyError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001B[0m in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001B[0m in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001B[0m in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001B[0m in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'label'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_27472\\2096798537.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[0mtrain_data_1\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtrain_data\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mfeatures_columns\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m \u001B[0mtest_data_1\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtest_data\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mfeatures_columns\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 5\u001B[1;33m \u001B[0mtarget\u001B[0m \u001B[1;33m=\u001B[0m\u001B[0mtrain_data\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'label'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001B[0m in \u001B[0;36m__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3805\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcolumns\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mnlevels\u001B[0m \u001B[1;33m>\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3806\u001B[0m                 \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_getitem_multilevel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3807\u001B[1;33m             \u001B[0mindexer\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcolumns\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_loc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3808\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mis_integer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mindexer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3809\u001B[0m                 \u001B[0mindexer\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[0mindexer\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001B[0m in \u001B[0;36mget_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m   3802\u001B[0m                 \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_engine\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_loc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcasted_key\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3803\u001B[0m             \u001B[1;32mexcept\u001B[0m \u001B[0mKeyError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3804\u001B[1;33m                 \u001B[1;32mraise\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3805\u001B[0m             \u001B[1;32mexcept\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3806\u001B[0m                 \u001B[1;31m# If we have a listlike key, _check_indexing_error will raise\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'label'"
     ]
    }
   ],
   "execution_count": 110
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:34:23.861474Z",
     "start_time": "2024-09-18T13:34:23.851834Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def model(train_1,target_1):\n",
    "    X_train, X_val, y_train, y_val = train_test_split(train_1, target_1, test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "    clf = XGBClassifier()\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = clf.predict_proba(X_train)\n",
    "    y_train_pred_pos = y_train_pred[:,1]\n",
    "\n",
    "    y_val_pred = clf.predict_proba(X_val)\n",
    "    y_val_pred_pos = y_val_pred[:,1]\n",
    "\n",
    "    auc_train = roc_auc_score(y_train, y_train_pred_pos)\n",
    "    auc_test = roc_auc_score(y_val, y_val_pred_pos)\n",
    "\n",
    "    print(f\"Train AUC Score {auc_train}\")\n",
    "    print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_val, y_val_pred_pos)\n",
    "    return fpr,tpr,clf,auc_test"
   ],
   "id": "cb36866174d2a1ae",
   "outputs": [],
   "execution_count": 111
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:34:32.765021Z",
     "start_time": "2024-09-18T13:34:31.813491Z"
    }
   },
   "cell_type": "code",
   "source": "fpr_1,tpr_1,clf_1,auc_test_1=model(train_data_1,target)#0.6153997",
   "id": "bc26f58c49bdc3e1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.8099121275136587\n",
      "Test AUC Score 0.5795435413715768\n"
     ]
    }
   ],
   "execution_count": 112
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:34:52.248380Z",
     "start_time": "2024-09-18T13:34:52.236844Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_auc_curve(fpr, tpr, auc):\n",
    "    plt.figure(figsize = (16,6))\n",
    "    plt.plot(fpr,tpr,'b+',linestyle = '-')\n",
    "    plt.fill_between(fpr, tpr, alpha = 0.5)\n",
    "    plt.ylabel('True Postive Rate')\n",
    "    plt.xlabel('False Postive Rate')\n",
    "    plt.title(f'ROC Curve Having AUC = {auc}')"
   ],
   "id": "8a06feff9633aad",
   "outputs": [],
   "execution_count": 113
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:34:59.235799Z",
     "start_time": "2024-09-18T13:34:59.089081Z"
    }
   },
   "cell_type": "code",
   "source": "plot_auc_curve(fpr_1, tpr_1, auc_test_1)",
   "id": "8fd473024f1e065e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 114
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:35:09.562459Z",
     "start_time": "2024-09-18T13:35:09.555363Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_learning_cuve(model, X, Y,num):\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 11)\n",
    "    train_loss, test_loss = [], []\n",
    "    \n",
    "    for m in range(num,len(x_train),num):\n",
    "        \n",
    "        model.fit(x_train.iloc[:m,:], y_train[:m])\n",
    "        y_train_prob_pred = model.predict_proba(x_train.iloc[:m,:])\n",
    "        train_loss.append(log_loss(y_train[:m], y_train_prob_pred))\n",
    "        \n",
    "        y_test_prob_pred = model.predict_proba(x_test)\n",
    "        test_loss.append(log_loss(y_test, y_test_prob_pred))\n",
    "        \n",
    "    plt.figure(figsize = (15,8))\n",
    "    plt.plot(train_loss, 'r-+', label = 'Training Loss')\n",
    "    plt.plot(test_loss, 'b-', label = 'Test Loss')\n",
    "    plt.xlabel('Number Of Batches')\n",
    "    plt.ylabel('Log-Loss')\n",
    "    plt.legend(loc = 'best')\n",
    "    plt.show()"
   ],
   "id": "161ef58179472b0",
   "outputs": [],
   "execution_count": 115
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-18T13:35:34.071674Z",
     "start_time": "2024-09-18T13:35:17.144256Z"
    }
   },
   "cell_type": "code",
   "source": "plot_learning_cuve(XGBClassifier(), train_data_1, target,5000)",
   "id": "a4286789f3e9886f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ],
      "image/png": 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JNptvP4enkpOlKVN88z6EdwCAAESoBgAAgODkyTxnkgnP9u83Pc/Kh2dbt0p5eZ6///33ez+UCsYAylfhHQAAXsbqn6z+CQAAEJw2bDCT+K9fX3moZE5O5fBsyxbp8OGqnxUZKXXrJvXs6WrNm5vzYWHVT7YfTOEXAAAhgtU/AQAAAMmEZ99+WzE8y8qq+rU2mxmeWT4869lT6tLFvRU0mWwfAICQQagGAACA4FBYKC1fLn3+uQnO0tPN+eHDq359u3au0KxHD7Pt3l2KifFZyQAAIHARqgEAACDwOJ3Snj3SunWmff21GeZ58mTN911zjTRxonTmmVJ8vPfqCca5zgAAQI0I1QAAAOD/cnOlb75xhWjr1kkHDlR+XVycCcx69pQaNZKee84385wx2T4AACGHUA0AAAC+VduqnIWF0nffVeyF9v33pndaeRER0tlnS+ef72pdupiFAySzeMBzzzHPGQAAaBCEagAAAPCt7Gxp6lRpyBCpVSvpp58q9kDbsEE6caLyfR06mODsvPPM9le/Yv4zAABgGUI1AAAA1N57rD6cTik/Xzp40LSvvjLnx46Vtm+X9u+vfE98vCs8Kw3SWrTw7H2Z5wwAADQgm9N5aj/60GK32xUfH6/c3FzFxcVZXQ4AAIA1NmyQUlPNZP+1DZUsKZGOHJEOHXIFZVXtlz9X0wIC4eFm9c2LLnIFaF27uoZxAgAA+JC7WRE91QAAACA5HGa7dq2Zv6ymwOzwYROseUtxsXTddUz0DwAAAgqhGgAAQKhxOqWdO6Vly6QvvjCLAuzYYa6NHu3+cxISpKQkqXlz02raLy42K3jabKZX3F13VV6VEwAAIIAQqgEAAAS7I0fMCprr1pmeaF9/bc7VJi1NuvrqykFZUpJpkZH1q4tVOQEAQAAjVAMAAKiLhpzYvz4KCqRvv3UFaOvWST/8UPl10dFSz57SGWdIZ51lzk2YULn3mD99NgAAAD9CqAYAAFAX2dnS1KnSkCENGzzVFN45ndKePSY4Kw3RNmxwzY9WXpcurpU0+/SRevWSoqJc1zdsMKGaL3qPsSonAAAIAoRqAAAAdXHihNkWFppwy2ZrmPcpH941bix9840rRFu3Ttq/v/I9iYmuAK10Nc3TTmuY+uoiOZlFCQAAQMAjVAMAAHDHL79I778v/fe/0vr1UmamOd+nj9lGRJg5xqKjTQ+wyEizLW3lj929FhlpVtyUpKFDpd27TYBXXkSEdM45FUO0Ll08D/noPQYAAOARm9N56v9nFlrsdrvi4+OVm5uruLg4q8sBAAD+4uefpZUrpc8/N9vt262uyGjVyvQ8u/hiE6D96ldSTIzVVQEAAAQNd7MieqoBAABI0r59rgDt88+lnTsrXrfZpDPPNHORpaZKYWHSQw9Jf/+7OV9UZIZdJiSYxQIKC8321P2arpXuL18uffll1XX+8osJ0saObeA/CAAAAGpCqAYAAELTTz9VDNF27ap4PSzMDKvs39/0Cvv1r01oVmrDBrO96CLvT+x/zz1mLrXS97nrrsqrcgIAAMBShGoAACB41LRSZmZmxRCtdE60UmFhJrS6+GITpF10kel1ZoXk5Mr1+2JVTgAAALiNUA0AADS8msIub7/P1KnS4MFmdc7yIdpPP1V8bXi4GcZZPkTzZH5VJvYHAAAIaYRqAACg4ZWGXUOG1C2EKimRjh+X8vKkY8dMq2q/dDGBq6+WDhyo+IyICOncc10h2oUXSk2b1v0zJSdLU6bU/X5P3ofwDgAAwO/UOVTbsWOHunbtKkl67733VFJSouuvv142T5dvBwAAwa2gQNq2zex/9pn07bfVh2Ll98ufO37cs/c8cMCEaD16mADtmmukCy6QmjTx/udraL4K7wAAAOCROoVqTz31lP785z8rNzdX48eP15tvvimn06nPPvtML7zwgrdrBAAAgWT/fmnNGumTT6SvvpIyMsyqlpL0hz/U79lhYSYYa9rUbJs0kQ4dkvbsqfzaoiJp82bp2mulyy+v3/sCAAAAp7A5nU6npze1atVKixcv1rnnnqvk5GR98cUXKigo0EUXXaQjR440RJ0Nxm63Kz4+Xrm5uYrzZB4VAABggqstW0yI9tVXpp26imZ1zjlH6tevckhW036jRtKpveKzs2tfKZOhkwAAAHCTu1lRnXqqORwONWvWTNu2bVNsbKw6d+6sbdu2MfQTAABv8NWk/nWRkyOtXWvCszVrpHXrzPDM8mw2qWdP6eyzpc6dzfbgQenuuxsm7GKlTAAAAFigTqHa4MGDddVVV6mkpEQ33XSTMjMzde+99+qKK67wdn0AAISe+k7q78n71BTelZRIO3a4eqCtWWOGcp4qLk7q08fMWda3r3T++VJ8fMXXbNhgtoRdAAAACBJ1CtVeeeUVzZ8/XzExMRo2bJi+//579e3bV+PHj/d2fQAAoKGcGt4dOyZ9840rRFu7VqpqWocuXUx41revCdLOPFMKD/d9/VVhpUwAAAD4SJ3mVAsmzKkGAPAL2dkm0Fq9Wlq2zKyQ2bWrlJBgAqvSOcUiI10tIqLisafn9+yRxo6V/u//pB9+MO9ZXFyxrpgYKS3NFaL16SM1b163z+evQ1oBAACActzNiuocqn366ae6/PLLdeLECb300ksqKSnRPffco9jY2DoXbQVCNQCAZQoLpS+/lJYskRYsMKtYWq1NG+mii1wh2tlnmwAOAAAACBENulDBmDFj9P777yszM1MPPvig1q5dK5vNpnXr1umf//xnnYsGACDoHTggLV1qgrRPPpFyc13XwsJMiNWxo/Tvf5uJ/VNSzAqbjRub3mqFhZVbUZH757dvl3bvrr6+O++Upkxp8D8DAAAAEOjq1FPttNNO0+rVq3XGGWeoefPm2rJli06ePKlzzjlHueX/4yAA0FMNANCgSkqkjRtNiLZkiRniWf7/9CYlSVddJQ0aJF1xhZSYaCb1T02V1q/3/qT+2dmmSeZ97rqrYVbkBAAAAAJUg/ZUCwsLU3FxsTZs2KDk5GQlJydr06ZNatSoUZ0LBgAgaOTlSZ9+akK0jz6Sfvml4vVf/cqEaIMGmfnKfDnJf1WhGStyAgAAAB6rU6g2fPhw9e/fX06nUxMnTtT333+vW2+9VTfccIO36wMAwD/UNtH+jh2u3mirVpmhlqUaN5Yuv9yEaFdfLbVuXfN7sYIlAAAA4PfqvFDBsmXLFBMTo379+unHH3/U8uXLNWLECEVE1CmnswzDPwEAbjl1SGZBgQnPliyRFi+Wdu6s+PrOnV290fr1k6Kjram7JqzICQAAAFTS4Kt/BgtCNQCAW0pDtUmTpC1bzPDOY8dc1yMiTHh2zTUmSOva1bpaAQAAANRZg86pJknPPPOMXnrpJe3du1ft2rXTPffco/vuu6+ujwMAwD+cPCn9+KMZzrl+vQnQfvrJ1RPtiSdcr01KkgYPNiHa5ZdL/I8zAAAAQMioU6g2depULVy4UFOnTlXHjh21c+dOTZ06VTk5OZo0aZK3awQAoHp1GcJYXCzt2WOCsx07pB9+cO3v2VNxdc6a3HuvNHVq3WsHAAAAELDqNPyzbdu2Wrx4sc4555yycxs3btQ111yjrKwsb9bX4Bj+CQAB7tS5zko5nWbVzaqCsx9/NHOiVScuzgzfTEmRmjeXTj9dys+X/vIXae5c1/tUtZImAAAAgIDWoMM/IyMjlZeXV+FcXl6eIiMj6/I4AADq7uRJs/3oI+n99yuGZ+XnPDtVdLTUpYtpXbtWbM2bSzZbxddv2GBCtd69K4Z3AAAAAEJSnUK1Rx55REOHDtWYMWPUsWNH7dq1S7Nnz9Zjjz3m7foAAKgsO9u05culxx8356qafiAsTOrQwRWWlQ/QUlLMdQAAAACogzqFaqNHj1arVq00d+5cvfHGG2rXrp1efPFFXXfddd6uDwCAyv72N+mvf63++s03S5MnSx07SlFR3nnP5GTzTIZ7AgAAAFAd51QLJsypBgAB5v33pbvukg4dMkM0r7xSWrqUuc4AAAAAeEWDzqlWlZ9//lmdO3dWfn6+tx4JAIDL4cPSAw9Ib71ljs88U1qwQAoPN6Eac50BAAAA8CGvTSbjdDp1snSyaAAAvGnRIqlHDxOohYVJjz5qVvtMS7O6MgAAAAAhyms91STJdupKaQAA1MeRI9KDD0pvvmmOu3c3vdPOO8/1GuY6AwAAAGABr4ZqAAB4zYcfSnffLf3yi+mdNm6cNGWK1KhRxdclJ5vzAAAAAOBDbodqDz30UI3X8/Ly6l0MAADKyTG90954wxx362Z6p51/vqVlAQAAAEB5bodqOTk5tb5m+PDh9SoGABDiFi82vdOys03vtIcflh5/vHLvNAAAAACwmNuh2vz58yscf/vtt+revbsiIyO9XhQAIMTk5Ehjxkivv26OzzjD9E7r08fKqgAAAACgWnVe/fPiiy9Wdna2N2sBAISiJUuknj1NoGazSY88Im3cSKAGAAAAwK/VeaECp9PpzToAAKHm6FFp7FjTI02SunY1+xdcYGFRAAAAAOCeOvdUAwCgzpYuNb3TFiwwvdMefljatIlADQAAAEDAqHNPNQAAPHb0qPTQQ1LpPJ1dupj9Cy+0tCwAAAAA8FSde6rZbDZv1gEACHYff2x6p82fb3qnjR1reqcRqAEAAAAIQHUO1cLDwwnWACDQZGdLU6aYra/ea/t26Y47pKuukrKypM6dpVWrpFmzpNjYhq8DAAAAABpAnUK1xYsXa/fu3UpJSfF2PQCAhpSdLU2d6rtQbepUqV8/6dVXTe+0Bx+UNm+WLrqo4d8fAAAAABpQneZUGz16tFasWKGtW7eqc+fOOu200yRJTzzxhJ555hnNmTNH119/vVcLBQB4QWmYtmqVtHev5HRKJSWudupxVefcPV61yrzXgQNSp05m2Oevf23dZwcAAAAAL7I5nU6npzeNHz9eL7zwgpo2bSq73a5p06bpgQceUEJCgl566SXNmDFDmzZtaoByvc9utys+Pl65ubmKi4uzuhwA8L7166W33pI+/VT67jvfv/+ll0qPPy7FxEjJyaYBAAAAgJ9yNyuqU6iWkpKihQsXql+/fvrxxx/Vs2dP7dmzR61bt9b+/fvVtm1bnThxol4fwFcI1QAEpexs6d13pX/+U1q9uubXpqRI7dtLYWGuZrPV7fi776Rvv63+vSZPNvOsAQAAAICfcjcrqtPwz8jISGVlZam4uFhZWVmKiIhQTk6OEhMTVVhYqIiIOj0WAFAfBw5I//639I9/mKGX5f83k3POka64QmreXBo3Tpo7V+rd21zzZu+x7GzXENMNG6S77qr8XgAAAAAQBOqUfr3yyisaPny4fvvb3yo+Pl59+vTRsGHDFBMToz/+8Y/q2bOnt+sEAFTl0CHpP/8xPdJWrDBzmZXq00e66Sbpxhultm3NuQ0bzLZ3b1fQ5U1VBXQN9V4AAAAAYKE6hWqXXnqp9u3bp4MHD+q0005TSUmJvvjiC5133nl68sknNXr0aG/XCQAodeSI9P77Jkj773+l4mLXtbQ06f/+Txo6VDr9dMtKBAAAAIBgV69xmk6nUxs3blS7du10ySWXSJIef/xxrxQGACgnN1datMgM7fz0U6mw0HWtd28TpP3f/0kdOtT8nORkM6+ZL4Zh+vK9AAAAAMDH6rRQQW5urm6//XYtWrRI0dHRKigo0HXXXadXX3014Cb7Z6ECAH4hO1uaM0caOdIVQuXlSR98YHqkffyxVFDgen2vXq4grUsXa2oGAAAAgCDkblYUVpeHjx49WiUlJcrKytKJEyf0008/qbCwUPfee2+dCwaAkJadLU2dKu3aZXqjXX+9WVTgt781wVpBgXTmmeY127ZJmzdLf/wjgRoAAAAAWKROwz+XLl2q9evXK/l/vSnatGmj2bNnKzU11avFAUBIcDqllSvN/mWXSQ6H61rXrmaxgZtuknr0sKY+AAAAAEAldQrV2rVrp+XLl+v3v/992bnly5frdCbFBgD3ZWdLe/ZI06ZJH35ozjkcZqXOK66QbrnFhGw2m7V1AgAAAAAqqVOo9ve//12DBg3SP//5T3Xs2FG7du3SV199pSVLlni7PgAIXrNmSTNnVj6/b5/06qtSSoo0YIDv6wIAAAAA1KpOc6r169dP27Zt08UXXyybzaZLLrlEW7duVXR0tLfrA4Dg9O230ttvm/0mTaQHHzT7c+dK69ebNnKkdfUBAAAAAGpUp1BNktq2batHH31Uzz//vMaPH6+wsDBdcMEFbt27ZcsWpaWlKTExUePGjZO7C5Du3LlTzZo1q3R+8ODBstlsZW0APTsA+LNFi6S+faWsLKlzZ+mbb6Thw8213r1drXQVUAAAAACA36lzqFYVd8Ixh8OhwYMHKzU1Venp6crIyNCCBQtqvW/37t0aNGiQcnJyKl1bv369vvvuO+Xk5CgnJ0eLFi2qS/kA0LCcTmn6dOm666Tjx818aevWSd26WV0ZAAAAAMBDXg3VbG5Mpr106VLl5uZq1qxZ6tSpk6ZNm6Z58+bVet+gQYN05513Vjq/b98+OZ1O9ezZUwkJCUpISFDjxo3rVD8ANJiTJ6Xf/U6aONGEa/fdJy1dKpX2vk1OliZPpncaAAAAAAQIr4Zq7ti8ebP69Omj2NhYSVKvXr2UkZFR632LFy/W0KFDK53/+uuvVVxcrLZt26px48a6+eabq+zNVsrhcMhut1doANCgsrOl/v2lhQul8HDpxRelZ5+VIiNdr0lOlqZMIVQDAAAAgADh9uqfv/rVr2rsiVZQUODWc+x2uzp06FB2bLPZFB4erpycHCUmJlZ7X8eOHZWZmVnp/I4dO5SamqqZM2cqLCxMI0aM0MSJE/Xiiy9W+Zzp06dr6tSpbtUKAPW2fr30m9+Y+dMSE6V335UuvdTqqgAAAAAA9WRzurlKwGuvvebWA2+77bYar48fP16FhYWaNWtW2bmUlBStXbtWbdq0qfHezMxMdejQoca521auXKkbb7xRBw8erPK6w+GQw+EoO7bb7UpJSVFubq7i4uJqfH8A8Mi//iXddpt04oTUvbv0wQdmYQIAAAAAgN+y2+2Kj4+vNStyu6dabWGZu5o1a6YtW7ZUOJeXl6eoqCivPD8hIUGHDh2Sw+FQdHR0pevR0dFVngcArykpkR5/XCrtFXvVVdLbb0vx8dbWBQAAAADwGp/PqZaWlqa1a9eWHWdmZsrhcKhZ6WTdHrrxxhsrPO+bb75Rq1atCM4AWCM/X7rpJleg9vDD0ocfEqgBAAAAQJDxeajWr18/5ebm6vXXX5ckzZgxQwMGDFB4eLjsdrsKCws9el6vXr00duxYrVu3TosXL9akSZN07733NkTpAFCzffukX//azJsWGSnNmyfNnGkWJwAAAAAABBW3h3967Q0jIvTyyy9r2LBhGjdunIqLi7Vy5UpJJiCbPXu2rr32WrefN2HCBO3Zs0eXX365WrRooXvuuUcTJkxooOoBoBrr1knXXiv98ouUlCS995500UVWVwUAAAAAaCBuL1TgbVlZWUpPT1ffvn3VvHlzK0qQ5P7kcwBQrTfflO68U3I4pLPOMgsStG9vdVUAAAAAgDrw+kIF3tamTZtaV/sEAL9WUiL98Y/SjBnmeMgQE7A1bWptXQAAAACABufzOdUAICjk5UnXXecK1CZMMEM+CdQAAAAAICRY1lMNAAJWZqbplfbdd1J0tFmQ4NZbra4KAAAAAOBDhGoA4InVq00PtUOHpJYtpfffl/r0sboqAAAAAICPMfwTANz16qvSpZeaQO1Xv5K++YZADQAAAABCFKEaAFQnO1uaMkXat096+GHpjjukwkLpxhulL76QUlKsrhAAAAAAYBGGfwJAdbKzpalTpU8+kdasMeemTJEmTZLC+N8kAAAAACCUEaoBQFV++UX6/HOzv2aNFBMjvfaaNHSopWUBAAAAAPwDoRoAHD8ubdggrVtngrT1602oViohQXruOalTJ9N7LTnZqkoBAAAAAH6CUA1AaCkulrZtMwHa11+b7ZYt5nx1jh6Vfvtbsz95shkCCgAAAAAIaYRqAAJPdrY0Z440cmTtvcaysioGaOnp0rFjlV/XurV0/vlS9+5Su3bSmWdK27dLd90lzZ0r9e5tXkcvNQAAAACACNUABKLSBQSGDKkYcuXlmaGb69a5grSsrMr3N24spaVJ551ngrTzz5fatKn6dZIJ1EpDNQAAAAAARKgGIJDt2GF6npUGaBkZUklJxdeEhUk9e7rCs/POM73QwsOtqRkAAAAAEBQI1QAEhuxs0w4elCZMMOduuaXy61JSXOHZ+edLqamuHmeeSk42c6gx5BMAAAAAcApCNQCBYc4cM+SzOjfdJP3tb94NwJKTWZQAAAAAAFAlQjUA/q+oSDp82HXctq20b58J2s4915xLTqZHGQAAAADAZwjVAPi3vXvNMM8vvzTH99wj/e53Ut++JlBjAQEAAAAAgAXCrC4AAKq1ZIl0zjkmUGvaVPrHP6QXXpCio62uDAAAAAAQ4gjVAPifwkJp3DjpmmukI0dMb7SNG6X/+z9znQUEAAAAAAAWY/gnAP+yZ490883S2rXm+P77pb/+tWLvNBYQAAAAAABYjFANgP9YtEi6/Xbp6FEpPl569VXp+uutrgoAAAAAgEoY/gnAegUF0pgx0rXXmkDtvPPMcE8CNQAAAACAnyJUA2CtXbukCy+U/v53c/zQQ9IXX0gdOlhbFwAAAAAANWD4JwDr/Pvf0u9/L9ntUmKi9Npr0uDBVlcFAAAAAECt6KkGwPdOnpTuu0+68UYTqPXtK23aRKAGAAAAAAgYhGoAfOuHH0yI9vzz5vgPf5A+/1xq187SsgAAAAAA8ATDPwH4zjvvSHffLeXlSUlJ0uuvS1ddZXVVAAAAAAB4jJ5qABreiRPSyJHSLbeYQO3XvzbDPQnUAAAAAAABilANQMPavl3q00d6+WXJZpP+9Cdp+XKpTRurKwMAAAAAoM4Y/gmg4bz5pjRqlHT8uNSihTm+/HKrqwIAAAAAoN7oqQbAe7KzpSlTpB9/lH7/e+l3vzOB2iWXmOGeBGoAAAAAgCBBTzUA3pOdLU2dahYg2L3bDPecPNkM+QwPt7o6AAAAAAC8hlANgHc4ndIHH5j93bulVq2kt94yvdQAAAAAAAgyDP8EUD/Z2dJnn0kDB5peapLUvbvprRYfb64DAAAAABBkCNUA1M/YsdKAAdKnn7rObdsmXXGFlJoqzZljXW0AAAAAADQQhn8CqJucHOnBB6V//MMcd+woDRsm/fnP0ty5Uu/e5nxysnU1AgAAAADQQOipBsBzH38s9ewpvfGGFBYm/eEP0tat0nXXmeu9e7saoRoAAAAAIAjRUw2A+/LypIcfNj3RJKlLF+m116QLLrC2LgAAAAAAfIyeagDcs2KFdNZZrkDtgQekTZsqBmrJydLkyfROAwAAAAAEPXqqAajZ8ePShAnSs8+a4/btpfnzpYsvrvza5GRpyhQfFgcAAAAAgDUI1QBU76uvpNtuk3buNMcjR0p//avUtKm1dQEAAAAAYDGGfwKo7ORJadw46aKLTKDWpo1ZnOCllwjUAAAAAAAQPdUAnOqbb0zvtG3bzPFtt0mzZ0sJCVZWBQAAAACAX6GnGgCjoECaNMksPLBtm9SypbRokbRgAYEaAAAAAACnoKcaAGnzZtMjbfNmc3zTTdLzz0unnWZtXQAAAAAA+Cl6qgGhrKhI+stfpLQ0E6iddpr0z39K77xDoAYAAAAAQA3oqQaEqm3bTO+0b74xx9deaxYiaNnS0rIAAAAAAAgE9FQDQk1xsTRzpvSrX5lALT5eev116T//IVADAAAAAMBN9FQDgl12tjRnjjRypHT8uHT77dKXX5prV14pvfKK1KaNpSUCAAAAABBoCNWAYJedLU2dKp04IT33nJSfLzVpIs2aJd15p2SzWV0hAAAAAAABh1ANCHbZ2Wb71FNme8kl0quvSu3bW1Y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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 116
  },
  {
   "metadata": {
    "jupyter": {
     "is_executing": true
    },
    "ExecuteTime": {
     "start_time": "2024-09-18T13:35:34.073120Z"
    }
   },
   "cell_type": "code",
   "source": [
    "smote = SMOTE(random_state = 402)\n",
    "X_smote, Y_smote = smote.fit_resample(train_data,target)\n",
    "sns.countplot(Y_smote, edgecolor = 'black')"
   ],
   "id": "72dbb72fec85858c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "a0846f3964afc51e"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
